JP2019139513A - Information processing device, information processing method, and information processing program - Google Patents

Abstract

To make appropriate recommendation as a user moves.SOLUTION: An information processing device according to the present application includes an acquisition unit, an extraction unit, and a transmission unit. The acquisition unit acquires graph information, in which a plurality of nodes corresponding to a plurality of movement routes respectively, in which corresponding users who have moved correspondingly to the respective movement routes are associated with each other, is connected according to the similarity of the plurality of movement routes, and acquires movement information related to the movement of one user. The extraction unit searches the graph information starting from a starting node that is a starting point of the search of the graph information determined based on a predetermined criterion among the plurality of nodes of the graph information acquired by the acquiring unit, and extracts a similar movement route that is a movement route similar to the movement of the one user from the plurality of movement routes. The transmission unit transmits recommendation information related to the movement of the one user to a terminal device used by the one user on the basis of information on the corresponding user information with the similar movement route extracted by the extraction unit.SELECTED DRAWING: Figure 4

Description

  The present invention relates to an information processing apparatus, an information processing method, and an information processing program.

  2. Description of the Related Art Conventionally, a technique for recommending various information to a user according to the user's behavior has been provided. For example, there is provided a technique for proposing a recommended route in consideration of the influence on the environment while taking into account the characteristics of the user.

JP 2013-083486 A JP 2013-101051 A

Masajiro Iwasaki "Neighbor Search by Approximate k Nearest Neighbor Graph Using Tree-Structured Index", IPSJ Transactions, 2011/2, Vol. 52, No. 2. pp.817-828.

  However, in the above-described conventional technology, it may be difficult to make an appropriate recommendation according to the movement of the user. For example, in order to specify the moving means for the section from the departure place to the destination based on the user characteristic information, information on the user's destination is required. Therefore, for example, when the user's destination is unknown, it may be difficult to make an appropriate recommendation according to the movement of the user.

  The present application has been made in view of the above, and an object thereof is to provide an information processing apparatus, an information processing method, and an information processing program that make an appropriate recommendation according to a user's movement.

  The information processing apparatus according to the present application is a plurality of nodes corresponding to each of a plurality of movement paths, and a plurality of nodes associated with corresponding users who have moved corresponding to the respective movement paths are the plurality of movement paths. Graph information linked according to the similarity of the user, an acquisition unit that acquires movement information related to the movement of one user, and a plurality of nodes of the graph information acquired by the acquisition unit. A movement path of movement similar to the movement of the one user among the plurality of movement paths by searching the graph information starting from a starting node that is a starting point of the search of the graph information determined based on Based on the information of the corresponding user associated with the similar movement route extracted by the extraction unit and the similar movement route extracted by the extraction unit. A transmission unit that transmits the recommendation information related to the movement of the one user to the terminal apparatus, characterized by comprising a.

  According to one aspect of the embodiment, there is an effect that an appropriate recommendation can be performed according to the movement of the user.

FIG. 1 is a diagram illustrating an example of information processing according to the embodiment. FIG. 2 is a diagram illustrating an example of information processing according to the embodiment. FIG. 3 is a diagram illustrating a configuration example of the information processing system according to the embodiment. FIG. 4 is a diagram illustrating a configuration example of the information processing apparatus according to the embodiment. FIG. 5 is a diagram illustrating an example of a POI information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of a user information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of a movement route information storage unit according to the embodiment. FIG. 8 is a diagram illustrating an example of the index information storage unit according to the embodiment. FIG. 9 is a diagram illustrating an example of the graph information storage unit according to the embodiment. FIG. 10 is a diagram illustrating an example of the model information storage unit according to the embodiment. FIG. 11 is a flowchart illustrating an example of information processing according to the embodiment. FIG. 12 is a flowchart illustrating an example of the generation process according to the embodiment. FIG. 13 is a diagram illustrating an example of feature amount extraction according to the embodiment. FIG. 14 is a flowchart illustrating an example of a search process using graph information. FIG. 15 is a hardware configuration diagram illustrating an example of a computer that implements the functions of the information processing apparatus.

  Hereinafter, an information processing apparatus, an information processing method, and an information processing program according to the present application (hereinafter referred to as “embodiment”) will be described in detail with reference to the drawings. Note that the information processing apparatus, the information processing method, and the information processing program according to the present application are not limited by this embodiment. In the following embodiments, the same portions are denoted by the same reference numerals, and redundant description is omitted.

(Embodiment)
[1. Information processing)
An example of information processing according to the embodiment will be described with reference to FIGS. 1 and 2. 1 and 2 are diagrams illustrating an example of information processing according to the embodiment. In FIG. 1 and FIG. 2, the information processing apparatus 100 searches for graph data (graph information) in which the movement route is structured in a graph, thereby extracting a similar movement route (hereinafter also referred to as “similar movement route”), The case where recommended information is provided to the user based on the extracted information is shown. The travel route here is not limited to a route from one point to another point, but includes various information on the movement mode such as a change in the movement speed of the user and a change in the position during the movement. Including.

  In FIG. 1 and FIG. 2, the information processing apparatus 100 uses graph information in which a moving route is structured in a graph using vector data (also simply referred to as “vector”) corresponding to each moving route. Each movement route is associated with information such as the user (target user) who performed the action, the spot where the target user stopped after the movement, the time zone (time) during the movement, the weather, and the like. In FIG. 1 and FIG. 2, a point visited by the user U1 (hereinafter sometimes referred to as a “stop spot”) based on information such as a moving route of the user U1 that the information processing apparatus 100 is moving by walking. The recommended case will be described as an example. Note that the information used by the information processing apparatus 100 is not limited to a vector, and may be information in any format as long as the information can express the similarity of each movement route. For example, the information processing apparatus 100 may use graph information in which a movement route is structured in a graph using predetermined data and values corresponding to each movement route. For example, the information processing apparatus 100 may use graph information in which a movement path is structured in a graph using a predetermined numerical value (for example, a binary value or a hexadecimal value) generated from each movement path. For example, instead of the vector, any form of data may be used as long as the distance (similarity) between the data is defined.

  In the example of FIGS. 1 and 2, a case where a user's walking movement path is targeted as a movement path is shown as an example. However, the movement path is not limited to a walking movement path, and a movement path of a vehicle such as an automobile. Any movement mode may be used as a movement route. That is, the target movement route (object) may be any movement route (information) as long as it can be expressed as a vector.

[1-1. Configuration of information processing system]
Prior to the description of FIGS. 1 and 2, the configuration of the information processing system 1 will be described with reference to FIG. As illustrated in FIG. 3, the information processing system 1 includes a terminal device 10, an information providing device 50, and an information processing device 100. The terminal device 10, the information providing device 50, and the information processing device 100 are connected via a predetermined network N so as to be communicable by wire or wirelessly. FIG. 3 is a diagram illustrating a configuration example of the information processing system according to the embodiment. Note that the information processing system 1 illustrated in FIG. 3 may include a plurality of terminal devices 10, a plurality of information providing devices 50, and a plurality of information processing devices 100.

  The terminal device 10 is an information processing device used by a user. The terminal device 10 receives various operations by the user. Hereinafter, the terminal device 10 may be referred to as a user. That is, hereinafter, the user can be read as the terminal device 10. The terminal device 10 described above is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like.

  The terminal device 10 has a function such as a GPS (Global Positioning System) sensor, and detects and acquires user position information (sensor information). In addition, the terminal device 10 uses the location information of the base station that is performing communication, a predetermined wireless communication function such as WiFi (registered trademark) or Bluetooth (registered trademark), or a function such as a beacon. The position information may be detected and acquired. Hereinafter, the position information may be simply referred to as “position”. For example, the terminal device 10 may improve the estimation accuracy of the user's position by combining information such as GPS, the wireless communication function as described above, and a beacon.

  Moreover, the terminal device 10 may detect various sensor information not only with a GPS sensor etc. but with various sensors. Further, the terminal device 10 has functions of a gyro sensor and an acceleration sensor, and detects and acquires acceleration information (sensor information) in the movement of the user. Further, the terminal device 10 may have various functions such as a temperature sensor and an atmospheric pressure sensor, and may be able to detect and acquire environmental information where the user is placed such as temperature and atmospheric pressure. Moreover, the terminal device 10 may have various functions such as a heart rate sensor, and may be able to detect and acquire a user's biological information. For example, a user using the terminal device 10 may acquire a user's own context information by the terminal device 10 by wearing a wearable device that can communicate with the terminal device 10. For example, a user who uses the terminal device 10 wears a wristband type wearable device that can communicate with the terminal device 10, and the terminal device 10 acquires information on the user's own heartbeat (pulse) by the terminal device 10. It may be possible.

  In the example of FIG. 1, the terminal device 10 shows the case where it is a smart phone which user U1 uses. For example, the terminal device 10 transmits various information such as user movement data and context information detected (collected) by various sensors to the information processing apparatus 100. For example, the terminal device 10 transmits various pieces of information such as user movement data and context information to the information processing device 100 using a predetermined application or the like.

  For example, the context here may be a concept including various situations of the user based on the situation of the user and the terminal device 10 used by the user and the environment (background) of the user and the terminal device 10. In addition, the context information includes information indicating the physical environment in which the user is placed, such as time information indicating a time zone and the like, weather information indicating weather, and the like while the user is moving on the moving route. The context information may include various information such as the social environment in which the user is placed, the content of the content provided to the user, the content of the content that the user has reacted to, the user's attributes, the user's emotions, and the like. . For example, the context information includes a communication state of the terminal device 10 used by the user, information to be displayed, and the like. As described above, the context information may include various information while the user is moving on the movement route. The above is an example, and the context information is not limited to the above, and various information may be included. Note that the context information may include information corresponding to the movement data, information on the corresponding user, and the like.

  The information processing apparatus 100 is an information processing apparatus that extracts a similar movement path that is similar to the movement of one user from a plurality of movement paths by searching for graph information using a starting point vector as a starting point. The authentication device 100 is an information processing device that transmits recommended information for recommending a stop-on spot based on various information associated with the extracted similar movement route to the terminal device 10 of one user. For example, the information processing apparatus 100 acquires movement route information related to the movement of one user, and determines the starting point vector based on the moving route information and information related to the starting point vector that is a starting point for searching graph information.

  The information processing apparatus 100 is an information processing apparatus in which information for providing various information to a user or the like is stored. For example, when the information processing apparatus 100 obtains movement data (hereinafter also referred to as “query information” or “query”) of movement of one user from the terminal device 10, a movement route (vector information or the like) similar to the query is obtained. Search and extract similar travel routes. Then, the information processing apparatus 100 transmits, to the terminal apparatus 10, recommended information based on the stop spot associated with the extracted similar movement route, the user who performed the movement, context information when moving, and the like. Also, for example, the data provided by the information processing apparatus 100 to the terminal device 10 is information for referring to recommended stop spot position information, specific name, URL (Uniform Resource Locator), and other stop spot information. It may be. In addition, when the information processing apparatus 100 acquires movement data of one user's movement from the terminal device 10, the information processing apparatus 100 searches for a movement route (such as vector information) similar to the query and provides the search result to the terminal device 10. Good. In the example of FIGS. 1 and 2, when the information processing apparatus 100 acquires movement data of one user's movement from the terminal device 10, the information processing apparatus 100 searches for a movement route that is similar to the movement of one user, Provided to the terminal device 10 as a movement route.

  The information providing apparatus 50 is an information processing apparatus in which information for providing various information to the information processing apparatus 100 is stored. For example, the information providing device 50 may store travel route information collected from various external devices such as a web server. For example, the information providing apparatus 50 is an information processing apparatus that provides the information processing apparatus 100 with various types of information such as graph information, index information, and models.

[1-2. About graph information)
Here, the graph information will be described. Further, as illustrated in the graph information GR11 in FIG. 2, the information processing apparatus 100 performs information processing on graph data in which each vector (node) is connected by a directed edge. Note that the graph information as indicated by the graph information GR11 in FIG. 2 may be generated by the information processing apparatus 100, or the information processing apparatus 100 may include the graph information as indicated by the graph information GR11 in FIG. You may acquire from other external apparatuses, such as the provision apparatus 50 (refer FIG. 3).

  Further, the directed edge here means an edge that can trace data only in one direction. In the following, a source traced by an edge, that is, a node as a starting point is referred to as a reference source, and a destination traced by an edge, that is, a node as an end point is defined as a reference destination. For example, the directed edge connected from the predetermined node “A” to the predetermined node “B” indicates that the reference source is the node “A” and the reference destination is the node “B”. Note that the edge connecting the nodes is not limited to the directed edge, and may be various edges. For example, the edge connecting the nodes may be an edge having no direction to connect the nodes. For example, the edges connecting the nodes may be edges that can be referred to each other. For example, all the edges connecting the nodes may be bidirectional edges.

  For example, such an edge having the node “A” as a reference source is referred to as an output edge of the node “A”. Further, for example, an edge with the node “B” as a reference destination in this way is referred to as an input edge of the node “B”. In other words, the output edge and the input edge here are differences in which one of the two nodes connected to the directed edge is regarded as the center, and the one directed edge. Becomes the output edge and the input edge. In other words, the output edge and the input edge are relative concepts, and for one directional edge, when the node as the reference source is regarded as the center, the output edge is regarded as the center, and the node as the reference destination is regarded as the center. The input edge. In the present embodiment, since the edges are directed edges such as output edges and input edges, the directed edges may be simply referred to as “edges” below.

  For example, the information processing apparatus 100 performs processing on nodes corresponding to millions to hundreds of millions of travel paths, but only a part of them is illustrated in the drawing. In the example of FIG. 2, in order to simplify the description, the outline of the process will be described by illustrating eight nodes. For example, the information processing apparatus 100 acquires graph information including a plurality of nodes (vectors) as indicated by nodes N1, N2, N5, etc., as indicated by the graph information GR11 in FIG. In the example of FIG. 2, each node in the graph information GR11 is connected with edges (output edges) to a predetermined number of nodes from the closest distance to the node. For example, the predetermined number may be various values such as 2, 5, 10, and 100 depending on the purpose and application. For example, when the predetermined number is 3, the output edge is connected from the node N1 to the node having the shortest distance from the node N1 and the three nodes having the third closest distance. The distance as the index indicating the similarity may be any distance as long as it can be applied as the distance between the vectors (N-dimensional vectors), such as the Euclidean distance, the Mahalanobis distance, and the cosine distance. Various distances may be used.

  In addition, when “node N * (* is an arbitrary numerical value)” is described in this way, this indicates that the node is identified by the node ID “N *”. For example, when “node N1” is described, the node is a node identified by the node ID “N1”.

  Further, in the graph information GR11 in FIG. 2, the node N712 is connected to the edge E7 that is a directed edge toward the node N7. That is, the node N712 is connected to the node N7 by the edge E7. Thus, when “edge E * (* is an arbitrary numerical value)” is described, it indicates that the edge is an edge identified by the edge ID “E *”. For example, when “edge E11” is described, the edge is an edge identified by the edge ID “E11”. For example, the node E712 can be traced from the node N712 to the node N7 by the edge E7 connected with the node N712 as a reference source and the node N7 as a reference destination. In this case, the edge E7 which is a directed edge is an output edge when identified with the node N712 as the center, and an input edge when identified with the node N7 as the center. Moreover, the bidirectional arrow in the graph information GR11 in FIG. 2 indicates that the directed edge from both nodes to the other node is connected. For example, the bidirectional arrow between the node N2 and the node N5 in the graph information GR11 connects two edges, a directed edge from the node N2 to the node N5 and a directed edge from the node N5 to the node N2. Indicates that

  Further, the graph information GR11 in FIG. 2 may be a Euclidean space. Also, the graph information GR11 shown in FIG. 2 is a conceptual diagram for explaining the distance between the vectors and the like, and the graph information GR11 is a multidimensional space. For example, the graph information GR11 illustrated in FIG. 2 is illustrated in a two-dimensional manner to be illustrated on a plane, but is assumed to be a multidimensional space such as 100 dimensions or 1000 dimensions.

  Here, the distance between vector data shows the similarity of a movement path | route, and it is so similar that distance is short. In the present embodiment, the distance between the nodes in the graph information GR11 is set as the similarity between the corresponding objects. For example, it is assumed that the similarity of the target (movement route) corresponding to each node is mapped as the distance between the nodes in the graph information GR11. For example, it is assumed that the similarity between concepts corresponding to each node is mapped to the distance between the nodes. Here, in the example illustrated in FIG. 2, the similarity between objects with short distances between nodes in the graph information GR11 is high, and the similarity between objects with long distances between nodes in the graph information GR11 is low. For example, in the graph information GR11 in FIG. 2, the node identified by the node ID “N1” and the node identified by the node ID “N2” are close to each other, that is, the distance is short. Therefore, the object corresponding to the node identified by the node ID “N1” and the object corresponding to the node identified by the node ID “N2” have a high degree of similarity.

  Further, for example, in the graph information GR11 in FIG. 2, the node identified by the node ID “N7” and the node identified by the node ID “N354” are remote, that is, the distance is long. Therefore, the similarity between the object corresponding to the node identified by the node ID “N7” and the object corresponding to the node identified by the node ID “N354” is low.

[1-3. Example of vector generation)
Also, each node (vector) here corresponds to each object (movement path). If the movement route information (movement data) is numerical data indicating various modes of movement, it can be handled as vector data as it is. However, when the moving data includes information such as text, image, or sound, conversion to vector data is required. Therefore, in the example of FIGS. 1 and 2, a multi-dimensional (N-dimensional) vector generated by the feature amount extracted from the movement data of each movement route may be an object.

  For example, the information processing apparatus 100 may generate an N-dimensional vector from the movement data of each movement route using a model that extracts the characteristics of the movement data. In the example of FIGS. 1 and 2, the information processing apparatus 100 uses each model (model M1) identified by the model ID “M1” as shown in the model information storage unit 126 (see FIG. 10). A vector is generated from the movement data of the route. As described above, when “model M * (* is an arbitrary numerical value)” is described, it indicates that the model is a model identified by the model ID “M *”. For example, when “model M1” is described, the model is a model identified by the model ID “M1”. Further, as shown in the model information storage unit 126, the model M1 is a model used for use “feature extraction (movement route)”, that is, feature extraction from movement route data. “Model data MDT1” is indicated.

  For example, the information processing apparatus 100 calculates the value of each element (neuron) in the model M1 by inputting movement data of an arbitrary movement path to the model M1, and outputs the same information as the input movement data. . For example, the information processing apparatus 100 may extract the value of each element (neuron) in the intermediate layer as a feature amount and generate N-dimensional vector data corresponding to each movement path.

  Here, an example of generation of vector data corresponding to each movement route will be described with reference to FIG. FIG. 13 is a diagram illustrating an example of feature amount extraction according to the embodiment. FIG. 13 is a conceptual diagram of the model M1. Note that in FIG. 13, illustration of lines indicating connection relationships of elements (neurons) is omitted. As shown in FIG. 13, the model M1 includes an input layer IL, an intermediate layer CL, and an output layer OL. For example, the input layer IL of the model M1 is a layer to which movement data is input. The output layer OL is a layer that outputs the same information as the input movement data in response to an input to the input layer IL.

  In addition, for example, the most compressed compression layer RP at the center of the intermediate layer CL is a layer that represents the characteristics of the input movement data. For example, in the intermediate layer CL of the model M1, the portion from the input layer IL to the compression layer RP corresponds to a portion where encoding processing is performed. In the intermediate layer CL of the model M1, the portion from the input layer IL to the compression layer RP corresponds to a portion that performs processing for compressing the characteristics of the input movement data. For example, in the intermediate layer CL of the model M1, the portion from the compression layer RP to the output layer OL corresponds to a portion that performs decoding processing. In the intermediate layer CL of the model M1, the portion from the compression layer RP to the output layer OL corresponds to a portion that performs a process of restoring the compressed movement data.

  For example, the information processing apparatus 100 may use information such as the neuron NL1 and the neuron NL2 included in the compression layer RP as a vector. For example, when movement data of a certain movement path is input, the information processing apparatus 100 uses a vector element (one-dimensional value) as a value VE1 corresponding to the calculated neuron NL1 or a value VE2 corresponding to the neuron NL2. May be extracted as For example, when movement data of a certain movement route is input, the information processing apparatus 100 may extract the value VE1 corresponding to the calculated neuron NL1 as the first dimension element of the movement route vector. Further, for example, when movement data of a certain movement route is input, the information processing apparatus 100 extracts the value VE2 corresponding to the calculated neuron NL2 as the second dimension element of the vector of the movement route. Good. As described above, the information processing apparatus 100 may generate a vector corresponding to each movement route by inputting the movement data of each movement route to the model M1. Note that the information processing apparatus 100 may acquire a vector corresponding to each movement route from another external apparatus such as the information providing apparatus 50. Note that the information processing apparatus 100 may use, as each element of the vector, a value corresponding to each neuron itself, or a value obtained by multiplying a value corresponding to each neuron by a predetermined coefficient. In addition, in the examples of FIGS. 1 and 2, for the sake of simplicity of explanation, each element (value) of the vector is an integer, but each element (value) of the vector is a real number including a numerical value after the decimal point. It may be.

  Note that the information processing apparatus 100 may use not only the elements (neurons) of the compression layer RP but also information on other elements (neurons) in the intermediate layer CL as vectors. For example, the information processing apparatus 100 may use information such as the neuron NL3 of the encoding part and the neuron NL4 of the decoding part as a vector. For example, when movement data of a certain movement route is input, the information processing apparatus 100 uses a vector element (a one-dimensional value) as a value VE3 corresponding to the neuron NL3 and a value VE4 corresponding to the neuron NL4 that are calculated. May be extracted as Note that the above is an example, and the information processing apparatus 100 may perform feature extraction from movement data using various models without being limited to an auto encoder. For example, the information processing apparatus 100 generates a model that learns to identify a movement path (movement data) that does not match a movement path (movement data) that conforms to a specific movement pattern, and uses the intermediate layer as vector data. It may be extracted as For example, the model may be generated by a method of learning similarity such as a triplet loss. Further, the information processing apparatus 100 may perform feature extraction without using a model. For example, the information processing apparatus 100 may generate a vector by extracting information corresponding to the feature (feature) set by the administrator of the information processing apparatus 100 from the movement data. For example, the information processing apparatus 100 extracts a plurality of position information corresponding to the movement route and information corresponding to features (features) such as speed when the user moves on the movement route from the movement data, and generates a vector. May be.

  For example, the information processing apparatus 100 may acquire the model M1 from another external device such as the information providing apparatus 50. Note that the information processing apparatus 100 may generate the model M1 using the movement data MD1, MD2, MD354, MD512, and the like of each movement route stored in the movement route information storage unit 123 (see FIG. 7) as an input. For example, the terminal device 10 may generate the model M1 using the movement data MD1, MD2, MD354, MD512, and the like of each movement route as inputs. For example, the movement data MD1, MD2, MD354, MD512, and the like may include a plurality of pieces of position information corresponding to the movement path, speeds when the user moves along the movement path, and the like. For example, in the movement data such as movement data MD1, MD2, MD354, and MD512, the position information group of the user detected from the start point to the end point in the movement of the corresponding user and each position information of the position information group are detected. Information about time etc. may be included. Further, for example, the movement data may include information on an average movement speed, a maximum speed, a minimum speed, and the like between the start point and the end point in the movement of the corresponding user. In addition, for example, the movement data may include information on the position, time, period, or the like where the movement is stopped due to, for example, stopping in the movement of the corresponding user. Note that the above is an example, and the movement data may include various information detected in the movement of the corresponding user.

  The information processing apparatus 100 includes an input layer to which movement route information (movement data) of a movement route is input, an output layer, and any layer from the input layer to the output layer and belonging to layers other than the output layer. Each element belonging to each layer other than the output layer with respect to the movement path information input to the input layer, including one element and a second element whose value is calculated based on the first element and the weight of the first element The model M1 that outputs the same information as the information input to the input layer from the output layer may be generated by performing the calculation based on the first element and the weight of the first element with the element as the first element. . Note that the information processing apparatus 100 may acquire a model used for vector generation from another external apparatus such as the information providing apparatus 50.

[1-4. Processing example)
From here, the information processing apparatus 100 will be described by taking as an example a case where movement data of one user's movement is obtained from the user and information relating to a movement route similar to the movement of the one user is provided. In the example of FIGS. 1 and 2, the terminal device 10 is used by a user U1 who is a predetermined administrator. For example, the user U1 may be a user who is walking around a predetermined area. In the example of FIG.1 and FIG.2, the case where the user U1 desires the information of the stop spot in the predetermined area where he walks is shown.

  First, the area information AR1 shown in FIG. 1 will be briefly described. In the example of FIG. 1, the area information AR1 is a diagram schematically showing a predetermined area, and is a terminal device used by a user identified by a user ID “U1” (hereinafter sometimes referred to as “user U1”). 10 shows a case where a stop spot is recommended to the user U1 using various pieces of movement data such as position information acquired from 10. In the example of FIG. 1, the information processing apparatus 100 acquires the position of the terminal device 10 used by the user U1 by using a function such as a GPS sensor included in the terminal device 10 used by the user U1. The area information AR1 in FIG. 1 schematically shows a route RT10 in the movement of the user U1 in a predetermined area, a POI (Point of Interest) located in the predetermined area, and the like. The area information AR1 shown in FIG. 1 schematically shows the position of the user U1 and the positions of the spots SP1 to SP3 that are POIs located in a predetermined area.

  In the example of FIG. 1, the user U1 moves from the position LC10 to the position LC15 by moving along the route RT10. In the example of FIG. 1, although illustrated with abstract codes such as position information LC10 and LC3, the position information may be information (latitude / longitude information) indicating specific latitude and longitude. Hereinafter, “position information LC * (* is a character string such as an arbitrary number)” may be referred to as “position LC *”. For example, when “position LC1” is described, it indicates that the position corresponds to the position information LC1.

  For example, the user U1 moves from the position LC10 that is the start point of the route RT10 to the position LC15 that is the end point of the route RT10. For example, the terminal device 10 detects (collects) position information when the user U1 moves along the route RT10 using various sensors such as a GPS sensor. For example, the terminal device 10 detects (collects) a plurality of pieces of position information when the user U1 moves along the route RT10 using various sensors such as a GPS sensor. For example, the terminal device 10 detects position information when the user U1 moves along the route RT10 using various sensors such as a GPS sensor, and collects a combination of the position information and the detection date and time of the position information. For example, the terminal device 10 identifies the moving speed of the user U1 and the position and date of the user U1 based on the combination of the position information when the user U1 moves along the route RT10 and the detection date and time of the position information. In addition, for example, the terminal device 10 specifies time information such as the date and time when the user U1 moves the route RT10 based on the combination of the detection date and time of the position information when the user U1 moves the route RT10. Further, for example, the terminal device 10 specifies weather information such as weather when the user U1 moves the route RT10 by a combination of the position information when the user U1 moves the route RT10 and the detection date and time of the position information. . For example, the terminal device 10 may acquire movement data when the user U1 moves along the route RT10 by a predetermined application that has been installed.

  Then, the terminal device 10 transmits to the information processing device 100 movement path information when the user U1 moves from the position LC10 to the position LC15. The terminal device 10 transmits movement data MD10 including various information such as position information detected while the user U1 moves from the position LC10 to the position LC15 to the information processing apparatus 100. For example, the terminal apparatus 10 transmits to the information processing apparatus 100 movement data MD10 including various information such as a position information group, movement speed, weather information, and time information when the user U1 moves from the position LC10 to the position LC15. . Note that the terminal device 10 may transmit movement data detected by the sensor in real time to the information processing device 100. For example, the terminal device 10 may transmit movement data to the information processing apparatus 100 as needed every time it is detected by a sensor. In the example of FIG. 1, the terminal device 10 transmits user information that identifies the user U1. For example, the terminal apparatus 10 transmits a user ID “U1” to the information processing apparatus 100 as information for specifying the user U1.

  Then, the information processing apparatus 100 acquires travel route information from the terminal device 10 used by the user U1 (step S11). For example, the information processing apparatus 100 acquires the movement data MD10 from the terminal device 10 used by the user U1. For example, the information processing apparatus 100 acquires movement data MD10 including various information such as a position information group, a moving speed, weather information, and time information when the user U1 moves from the position LC10 to the position LC15. In the example of FIG. 1, the information processing apparatus 100 includes a position information group detected when the user U1 moves from the position LC10 to the position LC15, and a daytime when the time when the user U1 moves from the position LC10 to the position LC15 is 12:00. Information indicating that it is the time zone of is acquired. Note that the information processing apparatus 100 may acquire weather information indicating that the weather when the user U1 has moved from the position LC10 to the position LC15 was clear from the terminal apparatus 10.

  Further, the information processing apparatus 100 specifies that the user U1 is a male in his twenties based on information specifying the user U1 and attribute information of the user U1 stored in the user information storage unit 122. Note that the information processing apparatus 100 may acquire information indicating that the user U1 is a male in his 20s from the terminal apparatus 10.

  In addition, the information processing apparatus 100 may acquire the combination of the position information and the detection date and time of the position information in the movement of the user U1 on the route RT10 from the terminal apparatus 10. In this case, for example, the information processing apparatus 100 determines the moving speed of the user U1 and the position and date of the user U1 based on the combination of the position information when the user U1 moves along the route RT10 and the detection date and time of the position information. You may specify. In addition, for example, the information processing apparatus 100 may specify time information such as the date and time when the user U1 moves the route RT10 based on the combination of the position information detection date and time when the user U1 moves the route RT10. . Further, for example, the information processing apparatus 100 specifies weather information such as weather when the user U1 moves the route RT10 based on a combination of the position information when the user U1 moves the route RT10 and the detection date and time of the position information. May be. For example, the information processing apparatus 100 acquires the weather information from the external apparatus by transmitting the position information when the user U1 moves along the route RT10 and the detection date and time of the position information to the external apparatus that provides the weather information. May be.

  Then, the information processing apparatus 100 performs an extraction process for extracting a similar movement route (step S12). The information processing apparatus 100 performs an extraction process of extracting a similar movement route similar to the movement of the user U1 based on the acquired movement data of the user U1. In the example of FIG. 1, the information processing apparatus 100 performs an extraction process using the movement data MD10.

  From here, the specific example of an extraction process is demonstrated using FIG. As illustrated in FIG. 2, the information processing apparatus 100 generates a vector used for searching for graph information from movement data corresponding to movement of one user. In the example of FIG. 2, the information processing apparatus 100 generates a vector corresponding to the movement of the user U1 on the route RT10 (hereinafter, may be referred to as “movement of the user U1”) by the process shown in the processing group PS11. To do. The information processing apparatus 100 inputs movement data MD10 relating to movement of the user U1 to the model M1 (step S12-1). Specifically, the information processing apparatus 100 inputs movement data MD10 of movement of the user U1 to the model M1. And the information processing apparatus 100 produces | generates a vector using the information in the model M1 after the input of the movement data MD10 (step S12-2). For example, the information processing apparatus 100 generates vector data using each element in the model M1 to which the movement data MD10 is input.

  In the example of FIG. 2, the information processing apparatus 100 generates vector data VD10 (also simply referred to as “vector VD10”) using the values of the elements in the model M1 to which the movement data MD10 is input. For example, the information processing apparatus 100 receives the value VE1 (see FIG. 13) corresponding to the neuron NL1 of the model M1 and the value VE2 (see FIG. 13) corresponding to the neuron NL2 when the movement data MD10 of the movement of the user U1 is input. ) To generate a vector. For example, when the movement data MD10 of the movement of the user U1 is input, the information processing apparatus 100 may extract the value VE1 corresponding to the calculated neuron NL1 as the first dimension element of the vector VD10. For example, when movement data of a certain movement route is input, the information processing apparatus 100 generates the vector VD10 using the calculated value VE2 corresponding to the neuron NL2 as a second-dimensional element of the vector VD10. In the example of FIG. 2, the information processing apparatus 100 generates a vector VD10 in which the first dimension element is “3” and the second dimension element is “2”.

  Then, the information processing apparatus 100 searches for a movement route (similar movement route) similar to the movement of the user U1 (step S12-3). For example, the information processing apparatus 100 may search for a similar movement route of the movement of the user U1 by appropriately using various conventional techniques such as the technique of neighborhood search disclosed in Non-Patent Document 1.

  In the example of FIG. 2, the information processing apparatus 100 searches for a similar movement route of the movement of the user U1 using the graph information GR11 and the index information IND11 as shown in the information group INF11. For example, the information processing apparatus 100 acquires the graph information GR11 regarding the travel route from the graph information storage unit 125 (see FIG. 9). Further, for example, the information processing apparatus 100 uses the index information storage unit 124 (see FIG. 8) to obtain the index information IND11 used to determine the node (hereinafter also referred to as “starting vector”) that is the starting point of the search in the graph information GR11. get. Note that the index information IND11 may be generated by the information processing apparatus 100, or the information processing apparatus 100 may acquire the index information IND11 from another external device such as the information providing apparatus 50.

  The information processing apparatus 100 uses the index information IND11 in order to determine (specify) the starting point vector corresponding to the movement (query) of one user. In the example of FIG. 2, the information processing apparatus 100 uses the index information IND11 in order to determine (specify) the starting point vector corresponding to the movement vector VD10 of the user U1. That is, the information processing apparatus 100 determines the starting vector in the graph information GR11 using the vector VD10 and the index information IND11.

  The index information IND11 in FIG. 2 has a hierarchical structure shown in the index information storage unit 124 in FIG. For example, the index information IND11 indicates that the first layer nodes (vectors) located immediately below the route RT are the nodes VT1, VT2, and the like. Further, for example, the index information IND11 indicates that the nodes in the second layer immediately below the node VT2 are nodes VT2-1 to VT2-4 (not shown). For example, the index information IND11 indicates that the nodes in the third hierarchy immediately below the node VT2-2 are the nodes N1, N2, and N5, that is, the nodes (vectors) in the graph information GR11.

  For example, the information processing apparatus 100 determines a starting point vector in the graph information GR11 using tree structure type index information as indicated by the index information IND11 in FIG. 2 (step S12-4). In the example of FIG. 2, the information processing apparatus 100 generates the vector VD10 and then traces the index information IND11 from the top to the bottom, thereby efficiently specifying the starting vector that is a neighborhood candidate for the index information IND11. A starting vector corresponding to the search query (movement data of one user) can be determined.

  For example, the information processing apparatus 100 may determine the starting vector corresponding to the vector VD10 by tracing the index information IND11 from the root RT to the leaf node (node (vector) in the graph information GR11). In the example of FIG. 2, for example, the information processing apparatus 100 determines the node N2 as a starting vector by tracing the index information IND11 from the route RT to the node N2. For example, the information processing apparatus 100 may determine the leaf node that has reached the origin vector by tracing the index information IND11 from the root RT to the leaf node by appropriately using various conventional techniques related to the tree structure.

  For example, the information processing apparatus 100 may determine the starting point vector by tracing down the index information IND11 based on the similarity with the vector VD10. For example, the information processing apparatus 100 may determine which node such as the nodes VT1 and VT2 is to be traced from the route RT based on the similarity between the vector VD10 and the nodes VT1 and VT2. For example, the information processing apparatus 100 may determine to trace from the route RT to the node VT2 having the highest similarity to the vector VD10 among the nodes VT1, VT2, and the like. In addition, for example, the information processing apparatus 100 may determine to trace from the node VT2 to the node VT2-2 having the highest similarity with the vector VD10 among the nodes VT2-1 to VT2-4. Further, for example, the information processing apparatus 100 may determine to trace from the node VT2-2 to the node node N2 having the highest similarity to the vector VD10 among the nodes N1, N2, and N5.

  In the example of FIG. 2, for simplicity of explanation, a case where one starting point vector is determined is shown, but the information processing apparatus 100 may determine a plurality of starting point vectors. For example, the information processing apparatus 100 may determine a plurality of vectors (nodes) such as the nodes N1, N2, and N5 as the starting point vectors. Note that one or more nodes may be selected at random from the graph information GR11 at the start of the search without using the index information IND11, and may be used as a starting point vector, or one or more nodes specified in advance may be used as starting points. It can be a vector.

  Then, the information processing apparatus 100 searches the graph information GR11 to extract a similar movement route for the movement of the user U1 (step S12-5). For example, the information processing apparatus 100 extracts a node located in the vicinity of the node N2 as a similar movement route. For example, the information processing apparatus 100 extracts a node having a short distance from the node N2 as a similar movement route. For example, the information processing apparatus 100 extracts a node that can be reached from the node N2 as a similar movement route by tracing the edge from the node N2. For example, the information processing apparatus 100 extracts a predetermined number (for example, 2 or 10) of nodes as similar movement paths. For example, the information processing apparatus 100 may extract a similar movement route of the movement of the user U1 by a search process as illustrated in FIG. 14, but details will be described later. In the example of FIG. 2, the information processing apparatus 100 extracts the node N2, the node N1, the node N512, and the like as a similar movement route by searching the graph information GR11 with the node N2 as a starting point.

  Thereby, the information processing apparatus 100 extracts a similar movement route as shown in the movement route information storage unit 123 in FIG. For example, the information processing apparatus 100 extracts a movement route MP1 corresponding to the movement of the user U99, a movement route MP2 corresponding to the movement of the user U51, a movement route MP512 corresponding to the movement of the user U21, and the like. As shown in the movement route information storage unit 123 in FIG. 1, each user's movement route is associated with a visit point (stop-on spot) visited by the user after the movement corresponding to the movement route.

  Then, the information processing apparatus 100 extracts a corresponding visit point based on the visit point associated with the extracted similar movement route (step S13). For example, the information processing apparatus 100 extracts a visit point associated with the user's travel route having an attribute similar to the attribute of the user U1 from the visit points associated with the extracted similar travel route. Further, for example, the information processing apparatus 100 selects a visit point associated with the user's travel route in a time zone similar to the travel time zone of the user U1 among the visit points associated with the extracted similar travel route. Extract.

  In the example of FIG. 1, the information processing apparatus 100 extracts a visit point associated with a travel route in which the user attribute is a male in his twenties and the travel time zone is daytime as a corresponding visit point. In this case, the information processing apparatus 100 extracts a spot SP2 that is a visit point when a user U99 of a male in his twenties moves in the daytime as a corresponding visit point. In addition, the information processing apparatus 100 extracts a spot SP2 that is a visit point when a user U21 of a male in his twenties moves in the daytime as a corresponding visit point. In addition, in the example of FIG. 1, the information processing apparatus 100 also extracts a spot SP <b> 1, which is a visit point when a male user U111 in his twenties moves in the daytime, as a corresponding visit point. Further, the information processing apparatus 100 does not extract the spot SP3, which is a visit point when a female U51 in the 20s moves at night, as a corresponding visit point.

  As a result, the information processing apparatus 100 extracts, as the corresponding visit points, the visit points in the movement similar to the attribute of the user U1 and the context information when the user U1 moves, from the similar movement routes. In the example of FIG. 1, the information processing apparatus 100 extracts the spot SP2 as the corresponding visited point most, as shown in the POI information storage unit 121.

  Then, the information processing apparatus 100 determines a recommended spot based on the extracted corresponding visited spot (step S14). For example, the information processing apparatus 100 determines the most visited points among the extracted corresponding visited points as recommended points. In the example of FIG. 1, the information processing apparatus 100 determines the spot SP2 as a recommended spot.

  Then, the information processing apparatus 100 provides information regarding the determined recommended spot (step S15). In the example of FIG. 1, the information processing apparatus 100 transmits recommended spot information indicating that the spot SP2 is a recommended spot to the terminal device 10 used by the user U1. Thereby, the information processing apparatus 100 provides the recommended spot information indicating that the spot SP2 is the optimum stop spot in the movement of the user U1 to the terminal apparatus 10 used by the user U1.

  As described above, the information processing apparatus 100 extracts the similar movement route of the movement of the user U1 acquired from the user U1 using the graph information GR11 and the index information IND11. For example, the information processing apparatus 100 uses the index information IND11 to determine a starting vector in the graph information GR11 when extracting a similar movement route for the movement of the user U1. Then, the information processing apparatus 100 extracts the similar movement route of the movement of the user U1 by searching the graph information GR11 using the determined starting point vector as a starting point.

  Then, the information processing apparatus 100 determines a stop spot recommended for the user U1 based on the information on the visited point associated with the extracted similar movement route, and transmits the information to the terminal apparatus 10 used by the user U1. To do. Thereby, the information processing apparatus 100 can make an appropriate recommendation according to the movement of the user.

  For example, there is a method of identifying a visit point corresponding to each user's movement route by a technique such as deep learning. Since there are few, there is a problem that it is difficult to improve the identification accuracy without learning. For example, there is a method that uses a model generated by a learning method such as deep learning, inputs information on the travel route to the model, and identifies whether the travel route is suitable for a predetermined visit point from the output score. In the case where there are a wide variety of visiting points, since there are few examples of each visiting point, it is difficult to improve the accuracy of the model and it is difficult to improve the identification accuracy. On the other hand, the information processing apparatus 100 performs a search by using graph information obtained by graph-structured a plurality of vectors generated based on features extracted from each element of the movement route information (movement data), thereby obtaining a certain movement route. It is possible to appropriately extract a similar movement route similar to. Then, the information processing apparatus 100 can determine an appropriate visit point based on the visit point information associated with the similar movement route. Therefore, the information processing apparatus 100 can accurately determine what visit point (stop-on spot) is appropriate for a certain movement.

[1-5. Index information)
The index information (index data) illustrated in the example of FIG. 2 is an example, and the information processing apparatus 100 may search for graph information using various index information. For example, the information processing apparatus 100 may generate index data used at the time of search. For example, the information processing apparatus 100 generates a search index for searching for a high-dimensional vector as index data. The high-dimensional vector here may be, for example, a vector of several hundreds to thousands of dimensions, or a vector of more dimensions.

  For example, the information processing apparatus 100 may generate a search index related to a tree structure (tree structure) as shown in FIG. 2 as index data. For example, the information processing apparatus 100 may generate a search index related to a kd tree (k-dimensional tree) as index data. For example, the information processing apparatus 100 may generate a search index related to a VP tree (Vantage-Point tree) as index data.

  Further, for example, the information processing apparatus 100 may generate the index data having another tree structure. For example, the information processing apparatus 100 may generate various index data in which leaves of tree-structured index data are connected to graph data. For example, the information processing apparatus 100 may generate various index data in which the leaf of the tree-structured index data corresponds to a node in the graph data. Further, when performing a search using such index data, the information processing apparatus 100 may search for graph data from a leaf (node) that has arrived by tracing the index data.

  Note that the index data as described above is an example, and the information processing apparatus 100 may generate index data of any data structure as long as it can identify a query in the graph data at high speed. . For example, if the information processing apparatus 100 can identify the node in the graph information corresponding to the query at a high speed, the information processing apparatus 100 generates index data by appropriately using various conventional techniques such as a technique related to binary space division. May be. For example, the information processing apparatus 100 may generate index data having any data structure as long as it is an index that can be used to search for a high-dimensional vector. For example, the information processing apparatus 100 may use information related to a graph-type search index as described in Non-Patent Document 1 as index information. The information processing apparatus 100 can enable more efficient search regarding a predetermined target by using the index data and the graph data as described above.

[2. Configuration of information processing apparatus]
Next, the configuration of the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 4 is a diagram illustrating a configuration example of the information processing apparatus according to the embodiment. As illustrated in FIG. 4, the information processing apparatus 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The information processing apparatus 100 includes an input unit (for example, a keyboard and a mouse) that receives various operations from an administrator of the information processing apparatus 100 and a display unit (for example, a liquid crystal display) for displaying various types of information. You may have.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card). The communication unit 110 is connected to a network (for example, the network N in FIG. 3) by wire or wirelessly, and transmits and receives information to and from the terminal device 10 and the information providing device 50.

(Storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. As illustrated in FIG. 4, the storage unit 120 according to the embodiment includes a POI information storage unit 121, a user information storage unit 122, a movement route information storage unit 123, an index information storage unit 124, and a graph information storage unit 125. And a model information storage unit 126.

(POI information storage unit 121)
The POI information storage unit 121 according to the embodiment stores various types of information related to POI. FIG. 5 is a diagram illustrating an example of a POI information storage unit according to the embodiment. The POI information storage unit 121 illustrated in FIG. 5 includes items such as “POI_ID”, “POI name”, and “location”.

  “POI_ID” indicates identification information for identifying the POI. “POI” indicates a POI identified by the corresponding POI_ID. For example, “POI name” indicates the name of the POI identified by the corresponding POI_ID. “Location” indicates the location of the POI identified by the corresponding POI_ID.

  For example, in the example shown in FIG. 5, the POI (spot SP1) identified by POI_ID “SP1” indicates that the POI name is “XX tower” and the location of the spot SP1 is the position LC1. In the example of FIG. 5, the position is illustrated by an abstract code such as “LC1”, but the position information may be specific latitude and longitude information.

  The POI information storage unit 121 is not limited to the above, and may store various information according to the purpose. For example, the POI information storage unit 121 may store information on a business operator corresponding to the POI such as the business type of the business operator corresponding to the POI. Further, for example, the POI information storage unit 121 may store information on products and services provided in the POI.

(User information storage unit 122)
The user information storage unit 122 according to the embodiment stores various types of information regarding user attributes. For example, the user information storage unit 122 stores user attribute information. FIG. 6 is a diagram illustrating an example of a user information storage unit according to the embodiment. The user information storage unit 122 illustrated in FIG. 6 includes items such as “user ID”, “age”, “sex”, “home”, “work location”, and “interest”.

  “User ID” indicates identification information for identifying a user. For example, the user identified by the user ID “U1” corresponds to the user U1 illustrated in the example of FIG. “Age” indicates the age of the user identified by the user ID. The “age” may be a specific age of the user identified by the user ID, such as 35 years old. “Gender” indicates the gender of the user identified by the user ID.

  “Home” indicates location information of the user's home identified by the user ID. In the example illustrated in FIG. 6, “home” indicates an abstract code such as “LC11”, but may be latitude / longitude information or the like. For example, “home” may be a region name or an address.

  “Work location” indicates position information of the user's work location identified by the user ID. In the example shown in FIG. 6, “work location” shows an abstract code such as “LC12”, but it may be latitude / longitude information or the like. Further, for example, the “work location” may be an area name or an address.

  “Interest” indicates the interest of the user identified by the user ID. That is, “interest” indicates an object that is highly interested by the user identified by the user ID. In the example illustrated in FIG. 6, one “interest” is illustrated for each user, but may be plural.

  For example, in the example illustrated in FIG. 6, the age of the user identified by the user ID “U1” is “20s”, and the gender is “male”. For example, the user identified by the user ID “U1” indicates that the home is “LC11”. For example, the user identified by the user ID “U1” indicates that the work location is “LC12”. For example, the user identified by the user ID “U1” indicates that he / she is interested in “sports”.

  Note that the user information storage unit 122 is not limited to the above, and may store various types of information according to the purpose. For example, the user information storage unit 122 may store information on the demographic attribute of the user and information on the psychographic attribute. For example, the user information storage unit 122 may store information such as name, family structure, income, interest, and lifestyle.

(Movement route information storage unit 123)
The movement route information storage unit 123 according to the embodiment stores various types of information regarding the movement route. For example, the movement route information storage unit 123 stores a movement route ID and vector data. FIG. 7 is a diagram illustrating an example of a movement route information storage unit according to the embodiment. The movement route information storage unit 123 illustrated in FIG. 7 includes items such as “movement route ID”, “movement data”, “vector information”, “visit point”, “corresponding user”, and “context information”.

  “Movement path ID” indicates identification information for identifying a movement path (object). “Movement data” indicates information related to the movement route identified by the movement route ID. In the example of FIG. 7, the movement data is indicated by an abstract code such as “MD1”, but each movement data includes a plurality of pieces of position information corresponding to the movement path identified by the movement path ID and the user's movement path. It includes information on various moving routes such as the speed when moving. “Vector information” indicates vector information corresponding to a movement route (object) identified by the movement route ID. That is, in the example of FIG. 7, vector data (vector information) corresponding to an object is registered in association with a movement route ID that identifies the movement route (object).

  “Visit point” indicates a spot (visit point) where a user who has moved along the corresponding movement route has stopped after the movement. “Corresponding user” indicates a user who has traveled along the corresponding travel route. “Context information” indicates context information when the user moves along the corresponding movement route.

  For example, in the example of FIG. 7, the movement path (object) identified by the movement path ID “MP1” is associated with multidimensional (N-dimensional) vector information of “2, 4, 5, 2. Indicates that For example, for the movement route (movement route MP1) identified by the movement route ID “MP1”, multi-dimensional “2, 4, 5, 2. N-dimensional) vector information is extracted from the movement data MD1.

  For example, a user who has moved on the movement route MP1 has stopped by the spot SP2. For example, it is indicated that the user who has moved on the movement route MP1 is a male user U99 in his twenties. For example, the time corresponding to the movement route MP1 indicates that it was a daytime time zone. In addition, for example, during the movement corresponding to the movement route MP1, the weather is clear.

  The movement route information storage unit 123 is not limited to the above, and may store various types of information according to the purpose.

(Index information storage unit 124)
The index information storage unit 124 according to the embodiment stores various types of information related to the index. FIG. 8 is a diagram illustrating an example of the index information storage unit according to the embodiment. Specifically, in the example of FIG. 8, the index information storage unit 124 shows tree-structured index information. In the example of FIG. 8, the index information storage unit 124 includes items such as “root hierarchy”, “first hierarchy”, “second hierarchy”, “third hierarchy”, and the like. In addition to “first hierarchy” to “third hierarchy”, “fourth hierarchy”, “fifth hierarchy”, “sixth hierarchy”, and the like may be included depending on the number of index hierarchies.

  The “root hierarchy” indicates a hierarchy of the root (highest level) that is a starting point for determining an origin node using an index. The “first hierarchy” stores information for identifying (specifying) nodes (nodes or vectors in graph information) belonging to the first hierarchy of the index. The node stored in the “first hierarchy” is a node corresponding to the hierarchy directly connected to the root of the index.

  The “second hierarchy” stores information for identifying (specifying) nodes (nodes or vectors in graph information) belonging to the second hierarchy of the index. The node stored in the “second hierarchy” is a node corresponding to the hierarchy directly below the node of the first hierarchy. The “third hierarchy” stores information for identifying (specifying) a node (a node or a vector in graph information) belonging to the third hierarchy of the index. The node stored in the “third hierarchy” is a node corresponding to the hierarchy directly below the second hierarchy node.

  For example, in the example shown in FIG. 8, the index information storage unit 124 stores information corresponding to the index information IND11 in FIG. For example, the index information storage unit 124 indicates that the nodes of the first hierarchy are the nodes VT1 to VT3 and the like. The numerical value in parentheses below each node indicates a vector value corresponding to each node.

  Further, for example, the index information storage unit 124 indicates that the nodes of the second hierarchy immediately below the node VT2 are the nodes VT2-1 to VT2-4. Further, for example, the index information storage unit 124 indicates that the node in the third hierarchy immediately below the node VT2-2 is a node (vector) in the graph information GR11 of the node N1, the node N2, and the node N5.

  The index information storage unit 124 is not limited to the above, and may store various types of information according to the purpose.

(Graph information storage unit 125)
The graph information storage unit 125 according to the embodiment stores various types of information regarding the graph information. FIG. 9 is a diagram illustrating an example of the graph information storage unit according to the embodiment. In the example of FIG. 9, the graph information storage unit 125 includes items such as “node ID”, “movement route ID”, and “edge information”. The “edge information” includes information such as “edge ID” and “reference destination”.

  “Node ID” indicates identification information for identifying each node (target) in the graph data. The “movement route ID” indicates identification information for identifying a movement route (object).

  “Edge information” indicates information related to an edge connected to the corresponding node. In the example of FIG. 9, “edge information” indicates a case where the edge is a directed edge, and indicates information regarding an output edge output from a corresponding node. “Edge ID” indicates identification information for identifying an edge connecting nodes. “Reference destination” indicates information indicating a reference destination (node) connected by an edge. That is, in the example of FIG. 9, for a node ID for identifying a node, information for identifying an object (target) corresponding to the node and a reference destination to which a directed edge (output edge) from the node is linked ( Node) is registered in association with each other.

  For example, in the example of FIG. 9, the node (vector) identified by the node ID “N1” corresponds to the travel route (object) identified by the travel route ID “MP1”. Further, from the node identified by the node ID “N1”, the edge identified by the edge ID “E11” is connected to the node (vector) identified by the node ID “N712”. That is, in the example of FIG. 9, the node (vector) identified by the node ID “N1” can be traced to the node (vector) identified by the node ID “N712”.

  The graph information storage unit 125 is not limited to the above, and may store various types of information according to the purpose. For example, the graph information storage unit 125 may store the length of an edge connecting each node (vector). That is, the graph information storage unit 125 may store information indicating the distance between each node (vector).

(Model information storage unit 126)
The model information storage unit 126 according to the embodiment stores information related to the model. For example, the model information storage unit 126 stores model information (model data) generated by the generation process. FIG. 10 is a diagram illustrating an example of the model information storage unit according to the embodiment. The model information storage unit 126 illustrated in FIG. 10 includes items such as “model ID”, “use”, and “model data”. In FIG. 10, only the models M1 and M2 are illustrated, but a lot of model information such as M21 and M22 may be stored according to each application (target of prediction).

  “Model ID” indicates identification information for identifying a model. For example, the model identified by the model ID “M1” corresponds to the model M1 illustrated in the example of FIG. “Use” indicates the use of the corresponding model. “Model data” indicates data of a corresponding model associated with the model data. For example, “model data” includes information including nodes in each layer, functions adopted by the nodes, connection relationships between the nodes, and connection coefficients set for connections between the nodes.

  For example, in the example shown in FIG. 10, the model (model M1) identified by the model ID “M1” has a use of “feature extraction (movement route)” and is used for feature extraction from the input movement route information. Indicates that it will be used. The model data of the model M1 is model data MDT1.

  The model M1 (model data MDT1) is an input layer to which movement route information (movement data) of a predetermined movement route is input, an output layer, and any layer from the input layer to the output layer and other than the output layer A first element belonging to the layer, and a second element whose value is calculated based on the first element and the weight of the first element, and other than the output layer with respect to the movement path information input to the input layer The computer is configured to output information similar to the information input to the input layer from the output layer by performing an operation based on the first element and the weight of the first element, with each element belonging to each layer of the first element as the first element Is a model for making

  Further, it is assumed that the models M1, M2, etc. are realized by a neural network having one or a plurality of intermediate layers, such as DNN (Deep Neural Network). In this case, for example, the first element included in the models M1 and M2 corresponds to any node of the input layer or the intermediate layer. The second element corresponds to the next node, which is a node to which a value is transmitted from the node corresponding to the first element. The weight of the first element corresponds to a connection coefficient that is a weight considered for a value transmitted from a node corresponding to the first element to a node corresponding to the second element.

  Here, it is assumed that the models M21, M22 and the like are realized by a regression model represented by “y = a1 * x1 + a2 * x2 +... + Ai * xi”. In this case, for example, the first elements included in the models M21 and M22 correspond to input data (xi) such as x1 and x2. The weight of the first element corresponds to the coefficient ai corresponding to xi. Here, the regression model can be regarded as a simple perceptron having an input layer and an output layer. When each model is regarded as a simple perceptron, the first element can correspond to any node of the input layer, and the second element can be regarded as a node of the output layer.

  The model information storage unit 126 is not limited to the above, and may store various model information according to the purpose.

(Control unit 130)
Returning to the description of FIG. 4, the control unit 130 is a controller, and is stored in a storage device inside the information processing apparatus 100 by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. Various programs (corresponding to an example of an information processing program) are executed by using the RAM as a work area. The control unit 130 is a controller, and is realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The control unit 130 includes an input layer, an output layer, and an input layer to which movement route information (movement data) of the movement route is input by information processing according to the models M1, M2, and the like stored in the model information storage unit 126. A first element belonging to any layer from the layer to the output layer other than the output layer, and a second element whose value is calculated based on the weight of the first element and the first element , Input to the input layer by performing an operation based on the first element and the weight of the first element, with each element belonging to each layer other than the output layer as the first element for the movement path information input to the input layer The same information as the output information is output from the output layer.

  As illustrated in FIG. 4, the control unit 130 includes an acquisition unit 131, a generation unit 132, a determination unit 133, an extraction unit 134, and a transmission unit 135, and functions and operations of information processing described below. Realize or execute. Note that the internal configuration of the control unit 130 is not limited to the configuration illustrated in FIG. 4, and may be another configuration as long as the information processing described below is performed.

(Acquisition part 131)
The acquisition unit 131 acquires various types of information. For example, the acquisition unit 131 acquires various types of information from the storage unit 120. For example, the acquisition unit 131 includes a POI information storage unit 121, a user information storage unit 122, a movement route information storage unit 123, an index information storage unit 124, a graph information storage unit 125, a model information storage unit 126, and the like. Acquire various information. The acquisition unit 131 acquires various types of information from an external information processing apparatus. The acquisition unit 131 acquires various types of information from the terminal device 10 and the information providing device 50.

  The acquisition unit 131 is a graph in which a plurality of nodes corresponding to each of a plurality of movement paths and a plurality of nodes associated with corresponding users who have moved corresponding to the respective movement paths are connected according to similarity. Information and movement information related to movement of one user are acquired. In addition, the acquisition unit 131 displays graph information in which a plurality of nodes associated with visit points visited by a corresponding user in a movement corresponding to each of a plurality of movement paths are linked according to the similarity of the plurality of movement paths. get. In addition, the acquisition unit 131 acquires graph information in which a plurality of nodes associated with the attribute information of the corresponding user are linked according to the similarity of the plurality of movement routes, and the attribute information of one user. The acquisition unit 131 also includes graph information in which a plurality of nodes associated with context information in movement corresponding to each movement route are linked according to the similarity of the plurality of movement routes, and context in the movement of one user. Get information and. The acquisition unit 131 also includes graph information in which a plurality of nodes associated with time information in movement corresponding to each movement route are linked according to the similarity of the plurality of movement routes, and time in movement of one user. Get information and. The acquisition unit 131 also includes graph information in which a plurality of nodes associated with weather information in the movement corresponding to each movement route are linked according to the similarity of the plurality of movement routes, and the weather in one user's movement. Get information and. Further, the acquisition unit 131 acquires graph information in which a plurality of nodes are connected according to the similarity of a plurality of vectors corresponding to each of a plurality of movement paths.

  The acquisition unit 131 is a graph in which a plurality of vectors corresponding to each of a plurality of movement paths, and a plurality of vectors associated with corresponding users who have moved corresponding to the respective movement paths are connected according to similarity. Information and movement information related to movement of one user are acquired. In addition, the acquisition unit 131 acquires graph information in which a plurality of vectors associated with visit points visited by a corresponding user in a movement corresponding to each of a plurality of movement routes are linked according to similarity.

  Further, the acquisition unit 131 acquires graph information in which a plurality of vectors associated with attribute information of a corresponding user are linked according to similarity, and attribute information of one user. In addition, the acquisition unit 131 acquires graph information in which a plurality of vectors associated with context information in movement corresponding to each movement route are linked according to similarity, and context information in movement of one user.

  The acquisition unit 131 acquires graph information in which a plurality of vectors associated with time information in movement corresponding to each movement route are linked according to similarity, and time information in movement of one user. In addition, the acquisition unit 131 acquires graph information in which a plurality of vectors associated with weather information in movement corresponding to each movement route are linked according to similarity, and weather information in movement of one user.

  In addition, the acquisition unit 131 acquires graph information in which a plurality of vectors indicating features of a plurality of movement paths are connected according to similarity. In addition, the acquisition unit 131 acquires graph information in which a plurality of vectors whose elements are feature amounts extracted from each of a plurality of movement paths using a predetermined model are connected according to similarity. In addition, the acquisition unit 131 inputs information on a plurality of movement routes to a predetermined model, whereby a plurality of vectors having elements of the feature amounts of the plurality of movement routes to be extracted are connected according to similarity. Get the graph information.

  For example, the acquisition unit 131 acquires a plurality of nodes (vectors) that are targets of data search. For example, the acquisition unit 131 acquires a directed edge group including a plurality of nodes and a plurality of directed edges that connect each of the plurality of nodes.

  For example, the acquisition unit 131 acquires graph information (graph data) from an external information processing apparatus. For example, the acquisition unit 131 acquires graph information from the graph information storage unit 125. For example, the acquisition unit 131 acquires graph information. In the example of FIG. 1, the acquisition unit 131 acquires graph information GR11.

  For example, the acquisition unit 131 acquires index information (index data) from an external information processing apparatus. For example, the acquisition unit 131 acquires index information from the index information storage unit 124. For example, the acquisition unit 131 acquires tree structure type index information. In the example of FIG. 1, the acquisition unit 131 acquires index information IND11.

  In the example of FIG. 1, the acquisition unit 131 acquires travel route information from the terminal device 10 used by the user U1. For example, the acquisition unit 131 acquires the movement data MD10 from the terminal device 10 used by the user U1. In addition, the acquisition unit 131 acquires movement data MD10 including various information such as a position information group, a movement speed, weather information, and time information when the user U1 moves from the position LC10 to the position LC15. In addition, the acquisition unit 131 is a position information group detected when the user U1 moves from the position LC10 to the position LC15, and the time when the user U1 moves from the position LC10 to the position LC15 is a daytime period such as 12:00. Acquire information indicating that. The acquisition unit 131 may acquire weather information indicating that the weather when the user U1 has moved from the position LC10 to the position LC15 was clear from the terminal device 10.

  In the example of FIG. 2, as illustrated in the information group INF11, the acquisition unit 131 uses the graph information GR11 and the index information IND11 to search for a similar movement route for the movement of the user U1. For example, the acquisition unit 131 acquires graph information GR11 related to the travel route from the graph information storage unit 125 (see FIG. 9). Further, for example, the information processing apparatus 100 acquires index information IND11 used to determine a starting point vector serving as a starting point of search in the graph information GR11 from the index information storage unit 124 (see FIG. 8).

(Generator 132)
The generation unit 132 generates various types of information. For example, the generation unit 132 uses the learning data (movement route information) stored in the movement route information storage unit 123 to generate a model as shown in the model information storage unit 126. For example, the generation unit 132 generates a model (auto encoder) that outputs information similar to the input travel route information based on the learning data acquired by the acquisition unit 131. For example, the generation unit 132 generates a model (auto encoder) that outputs the same information as the input travel route information, using the input travel route information itself as correct information.

  For example, the generation unit 132 generates a model M1 and the like, and stores the generated model M1 and the like in the model information storage unit 126. Note that the generation unit 132 may generate the model M1 using any learning algorithm. For example, the generation unit 132 generates the model M1 using a learning algorithm such as a neural network. As an example, when the generation unit 132 generates a model M1 or the like using a neural network, the model M1 or the like includes an input layer including one or more neurons, an intermediate layer including one or more neurons, and one or more neurons. Including an output layer.

  The generation unit 132 is a first layer belonging to any layer other than the output layer, which is any of the input layer, the output layer, and the input layer to the output layer to which the movement route information (movement data) of the movement route is input. Each element belonging to each layer other than the output layer with respect to the movement path information input to the input layer, the element and a second element whose value is calculated based on the first element and the weight of the first element Is used as the first element, and a calculation based on the first element and the weight of the first element is performed to generate a model that outputs information similar to the information input to the input layer from the output layer.

  For example, the generation unit 132 generates a model based on the learning data. For example, the generation unit 132 generates a model based on the learning data. For example, the generation unit 132 generates a model by performing learning using the movement data MD1, MD2 and the like in the movement route information storage unit 123 as learning data (teacher data).

  For example, when the movement data MD1 is input, the generation unit 132 performs a learning process so that the model M1 outputs the same information as the movement data MD1. For example, when the movement data MD2 is input, the generation unit 132 performs a learning process so that the model M1 outputs the same information as the movement data MD2.

  The generation unit 132 is not limited to M1 as an auto encoder, and may generate a model M21 that predicts whether or not to stop at the spot SP1 using various learning algorithms. For example, the generation unit 132 generates a model M21 and the like using a learning algorithm such as a neural network, a support vector machine (SVM), clustering, and reinforcement learning. As an example, when the generation unit 132 generates a model M21 or the like using a neural network, the model M21 or the like includes an input layer including one or more neurons, an intermediate layer including one or more neurons, and one or more neurons. Including an output layer.

  The generation unit 132 generates a model and stores the generated model in the model information storage unit 126. Specifically, the generation unit 132 is any layer from the input layer, the output layer, and the input layer to the output layer to which the movement route information (movement data) of the movement route is input and other than the output layer. A first element belonging to the layer, and a second element whose value is calculated based on the weight of the first element and the first element, and for the movement path information input to the input layer, other than the output layer By using each element belonging to each layer as a first element and performing an operation based on the first element and the weight of the first element, a score value used for predicting whether or not the travel path stops at the spot SP1 is output from the output layer Generate the model to be used. For example, the generation unit 132 may be any layer from the input layer, the output layer, and the input layer to the output layer to which the movement route information (movement data) of a certain movement route is input, to a layer other than the output layer. And a second element whose value is calculated based on the first element and the weight of the first element, and for each of the layers other than the output layer with respect to the movement path information input to the input layer With each element belonging as a first element, a calculation is performed based on the first element and the weight of the first element, whereby a score value used for predicting whether the movement path is a movement path suitable for a predetermined application is obtained. A model M21 to be output from the output layer is generated. For example, the generation unit 132 generates the model M21 so as to output the score “1” when the movement route information of the movement route that stops at the spot SP1 is input. For example, the generation unit 132 generates the model M21 so as to output a score “0” when movement route information of a movement route that does not stop at the spot SP1 is input. Note that the information processing apparatus 100 may not include the generation unit 132 when acquiring a model from another external apparatus such as the information providing apparatus 50.

  In the example of FIG. 2, the generation unit 132 generates a vector used for searching for graph information from movement data corresponding to movement of one user. For example, the generation unit 132 generates a vector corresponding to the movement of the user U1 by a process as shown in the process group PS11. The information processing apparatus 100 inputs movement data MD10 related to the movement of the user U1 to the model M1. For example, the generation unit 132 inputs movement data MD10 of movement of the user U1 to the model M1. And the information processing apparatus 100 produces | generates a vector using the information in the model M1 after the input of the movement data MD10. For example, the generation unit 132 generates vector data using each element in the model M1 to which the movement data MD10 is input.

  In the example of FIG. 2, the generation unit 132 generates a vector VD10 using the value of each element in the model M1 to which the movement data MD10 is input. For example, the generation unit 132 receives the value VE1 corresponding to the neuron NL1 of the model M1 (see FIG. 13) and the value VE2 corresponding to the neuron NL2 (see FIG. 13) when the movement data MD10 of the movement of the user U1 is input. Is used to generate a vector. For example, the generation unit 132 may extract the value VE1 corresponding to the calculated neuron NL1 as the first dimension element of the vector VD10 when the movement data MD10 of the movement of the user U1 is input. For example, when movement data of a certain movement route is input, the generation unit 132 generates the vector VD10 using the calculated value VE2 corresponding to the neuron NL2 as the second dimension element of the vector VD10. For example, the generation unit 132 generates the vector VD10 in which the first dimension element is “3” and the second dimension element is “2”.

(Determining unit 133)
The determination unit 133 determines various types of information. For example, the determination unit 133 determines the starting point vector based on the movement route information acquired by the acquiring unit 131 and information on the starting point vector that is the starting point of the search for the graph information. The determining unit 133 determines the starting node based on the index information used for determining the starting node. Further, the determination unit 133 determines a starting node based on the tree structure type index information. For example, the determination unit 133 determines the starting point vector based on the movement route information and information that serves as a reference for determining the starting point vector for searching the graph information. Further, the determination unit 133 determines a starting point vector based on the index information used for determining the starting point vector. Also, the determination unit 133 determines a starting point vector based on the tree structure type index information.

  In the example of FIG. 2, the determination unit 133 uses the index information IND11 to determine (specify) the starting point vector corresponding to the movement vector VD10 of the user U1. That is, the determination unit 133 determines a starting point vector in the graph information GR11 using the vector VD10 and the index information IND11.

  For example, the determination unit 133 determines a starting point vector in the graph information GR11 using tree structure type index information as indicated by the index information IND11 in FIG. In the example of FIG. 2, after the determination unit 133 generates the vector VD10 and traces the index information IND11 from the top to the bottom, the determination unit 133 efficiently searches by specifying a starting vector that is a neighborhood candidate of the index information IND11. A starting vector corresponding to the query (movement data of one user) can be determined.

  For example, the determination unit 133 may determine the starting vector corresponding to the vector VD10 by tracing the index information IND11 from the root RT to the leaf node (the node (vector) in the graph information GR11). In the example of FIG. 2, for example, the determination unit 133 determines the node N2 as a starting vector by tracing the index information IND11 from the route RT to the node N2.

  In the example of FIG. 1, the determination unit 133 determines a recommended point based on the extracted corresponding visit point. For example, the determination unit 133 determines the most visited points among the extracted corresponding visited points as recommended points. For example, the determination unit 133 determines the spot SP2 as a recommended point.

(Extractor 134)
The extraction unit 134 extracts various information. For example, the extraction unit 134 includes a POI information storage unit 121, a user information storage unit 122, a movement route information storage unit 123, an index information storage unit 124, a graph information storage unit 125, a model information storage unit 126, and the like. Extract various information. The extraction unit 134 searches for graph information starting from a starting node that is a starting point for searching for graph information determined based on a predetermined criterion among a plurality of nodes of the graph information acquired by the acquiring unit 131. Thus, a similar movement route that is a movement route similar to the movement of one user is extracted from the plurality of movement routes. The extraction unit 134 extracts a similar movement route that is a movement route similar to the movement of one user by searching for a node included in the graph information. The extraction unit 134 searches the nodes included in the graph information by tracing the nodes included in the graph information based on the connection relation of the edges, and thus is similar to the movement route of the movement similar to the movement of one user. Extract travel routes. The extraction unit 134 extracts a similar movement route using the starting node determined by the determination unit 133 as a starting point. For example, the extraction unit 134 extracts various types of information based on the information acquired by the acquisition unit 131. The extraction unit 134 searches for graph information using a starting point vector as a starting point for searching for graph information determined based on a predetermined criterion among a plurality of vectors of graph information acquired by the acquiring unit 131. Thus, a similar movement route that is a movement route similar to the movement of one user is extracted from the plurality of movement routes. The extracting unit 134 extracts a similar movement route using the starting point vector determined by the determining unit 133 as a starting point. For example, the extraction unit 134 may use information on the length (distance) of edges connecting between the nodes (vectors) stored in the graph information storage unit 125, or may determine each node ( Information on the length (distance) of the edges connecting the vectors) may be calculated, and the information on the calculated length (distance) may be used.

  In the example of FIG. 1, the extraction unit 134 searches the graph information GR11 to extract a similar movement route for the movement of the user U1. For example, the extraction unit 134 extracts a node located in the vicinity of the node N2 as a similar movement route. For example, the extraction unit 134 extracts a node having a short distance from the node N2 as a similar movement route. For example, the extracting unit 134 extracts a node that can be reached from the node N2 as a similar movement route by tracing the edge from the node N2. For example, the extraction unit 134 extracts a predetermined number (for example, 2 or 10) of nodes as similar movement paths. For example, the extraction unit 134 may extract a similar movement route of the movement of the user U1 by a search process as shown in FIG. 14, but details will be described later. In the example of FIG. 2, the extracting unit 134 extracts the node N2, the node N1, the node N512, and the like as a similar movement route by searching the graph information GR11 with the node N2 as a starting point.

  For example, the extraction unit 134 extracts similar movement routes as illustrated in the movement route information storage unit 123 in FIG. For example, the extraction unit 134 extracts a movement route MP1 corresponding to the movement of the user U99, a movement route MP2 corresponding to the movement of the user U51, a movement route MP512 corresponding to the movement of the user U21, and the like.

  For example, the extraction unit 134 extracts a corresponding visit point based on the visit point associated with the extracted similar movement route. For example, the extraction unit 134 extracts a visit point associated with the user's travel route having an attribute similar to the attribute of the user U1 from the visit points associated with the extracted similar travel route. Further, for example, the extraction unit 134 extracts a visit point associated with the user's travel route in a time zone similar to the travel time zone of the user U1 from the visit points associated with the extracted similar travel route. To do.

  In the example of FIG. 1, the extraction unit 134 extracts a visit point associated with a travel route in which the user attribute is a male in his twenties and the travel time zone is daytime as a corresponding visit point. In this case, the extraction unit 134 extracts a spot SP2 that is a visit point when a user U99 of a male in his twenties moves in the daytime as a corresponding visit point. Further, the extraction unit 134 extracts a spot SP2 that is a visit point when a user U21 of a male in his twenties moves in the daytime as a corresponding visit point. Further, the extraction unit 134 extracts a spot SP1 that is a visit point when a user U111 of a male in his twenties moves in the daytime as a corresponding visit point. Further, the extraction unit 134 does not extract the spot SP3, which is a visit point when a female U51 in the 20s moves at night, as a corresponding visit point.

  For example, the extraction unit 134 extracts, as the corresponding visit points, the visit points in the movement similar to the attribute of the user U1 and the context information when the user U1 moves, from the similar movement routes. In the example of FIG. 1, as shown in the POI information storage unit 121, the extraction unit 134 extracts the spot SP <b> 2 most frequently as a corresponding visit point.

(Transmitter 135)
The transmission unit 135 provides various types of information. For example, the transmission unit 135 transmits various types of information to the terminal device 10 and the information providing device 50. For example, the transmission unit 135 distributes various types of information to the terminal device 10 and the information providing device 50. For example, the transmission unit 135 provides various types of information to the terminal device 10 and the information providing device 50.

  The transmission unit 135 transmits the recommended information related to the movement of one user to the terminal device 10 used by one user based on the information on the corresponding user associated with the similar movement route extracted by the extraction unit 134. To do. The transmission unit 135 transmits recommended information for recommending a visited place in the movement of one user based on the visited place of the corresponding user associated with the similar movement route.

  The transmission unit 135 transmits recommendation information based on a movement route in which the attribute information of the corresponding user associated with the similar movement route among the similar movement routes corresponds to the attribute information of one user. The transmission unit 135 transmits recommendation information based on a movement route in which context information associated with the similar movement route among the similar movement routes corresponds to the context information in the movement of one user.

  The transmission unit 135 transmits the recommended information based on the movement route in which the time information associated with the similar movement route among the similar movement routes corresponds to the time information in the movement of one user. The transmission unit 135 transmits recommendation information based on a travel route in which the weather information associated with the similar travel route among the similar travel routes corresponds to the weather information in the movement of one user.

  For example, the transmission unit 135 provides an object ID corresponding to the query as a search result. For example, the transmission unit 135 provides the object ID selected by the extraction unit 134 to the information providing apparatus 50. The transmission unit 135 provides the information providing apparatus 50 with the object ID selected by the extraction unit 134 as information indicating a vector corresponding to the query. The transmission unit 135 may provide the model generated by the generation unit 132 to an external information processing apparatus.

  In the example of FIG. 1, the transmission unit 135 provides information regarding the recommended spot determined by the determination unit 133. For example, the transmission unit 135 transmits recommended spot information indicating that the spot SP2 is a recommended spot to the terminal device 10 used by the user U1. For example, the transmission unit 135 provides the recommended spot information indicating that the spot SP2 is the optimum stop spot in the movement of the user U1 to the terminal device 10 used by the user U1.

[3. Information processing flow)
Next, a procedure of information processing by the information processing system 1 according to the embodiment will be described with reference to FIG. FIG. 11 is a flowchart illustrating an example of information processing according to the embodiment.

  As illustrated in FIG. 11, the information processing apparatus 100 acquires information (movement data) regarding a movement route of one user (step S <b> 101). In the example of FIG. 1, the information processing apparatus 100 acquires movement data MD <b> 10 related to the movement of the user U <b> 1 from the terminal device 10.

  The information processing apparatus 100 acquires graph information related to the movement route (step S102). For example, the information processing apparatus 100 acquires the graph information GR11 regarding the movement route from the graph information storage unit 125.

  Then, the information processing apparatus 100 generates a vector from the movement data of one user using the model (step S103). In the example of FIG. 1, the information processing apparatus 100 generates a vector VD10 from the movement data MD10 using the model M1 stored in the model information storage unit 126.

  Then, the information processing apparatus 100 extracts a similar movement route of the movement route of one user by searching for graph information (step S104). For example, the information processing apparatus 100 determines a starting point vector using the generated vector and index information. In the example of FIG. 1, the information processing apparatus 100 determines the starting vector as the node N2 using the vector VD10 and the index information IND11 stored in the index information storage unit 124. In the example of FIG. 1, the information processing apparatus 100 extracts the node N2, the node N1, and the node N512 as similar movement paths by searching the graph information GR11 with the node N2 as a starting point.

  Then, the information processing apparatus 100 extracts a corresponding visit point from the extracted travel route based on the attribute information and the context information (Step S105). In the example of FIG. 1, the information processing apparatus 100 extracts a visit point associated with a travel route in which the user attribute is a male in his twenties and the travel time zone is daytime as a corresponding visit point.

  Then, the information processing apparatus 100 determines a recommended visit point based on the extracted number of corresponding visit points (step S106). In the example of FIG. 1, the information processing apparatus 100 determines a spot SP2 that is the most visited among the extracted spots in the corresponding visited points as a recommended point.

  Then, the information processing apparatus 100 transmits information on recommended visit points (step S107). In the example of FIG. 1, the information processing apparatus 100 transmits information on the spot SP2 to the terminal apparatus 10 used by the user U1 as a recommended visit point.

[4. Generation process flow)
Next, a generation process procedure performed by the information processing system 1 according to the embodiment will be described with reference to FIG. FIG. 12 is a flowchart illustrating an example of the generation process according to the embodiment.

  As illustrated in FIG. 12, the information processing apparatus 100 acquires learning data (step S201). For example, the information processing apparatus 100 acquires learning data from the movement route information storage unit 123. For example, the information processing apparatus 100 acquires movement data MD1, MD2, and the like as learning data from the movement route information storage unit 123.

  Thereafter, the information processing apparatus 100 generates a model based on the learning data (step S202). For example, the information processing apparatus 100 generates a model M1 using learning data from the movement route information storage unit 123. For example, the information processing apparatus 100 generates the model M1 so that information (movement data) similar to information (movement data) input to the input layer is output from the output layer. For example, the information processing apparatus 100 generates a model M1 as an auto encoder that receives movement data of a movement route as an input.

[5. Search example)
Here, an example of the search using the graph information described above is shown. The search using the graph information (graph data) is not limited to the following, and may be performed by various procedures. This point will be described with reference to FIG. 14 as an example. FIG. 14 is a flowchart illustrating an example of a search process using graph data (graph information). Further, the object described below may be read as a vector or a node. In the following description, it is assumed that the information processing apparatus 100 performs a search process, but the search process may be performed by another apparatus. For example, the information processing apparatus 100 uses vector data generated from movement data of one user's movement as a search query. For example, the information processing apparatus 100 searches for graph data using a starting point vector determined based on vector data generated from movement data of one user's movement and index information as a starting point. In the example of FIG. 1, the information processing apparatus 100 searches the graph information GR11 using the node N2, which is a starting vector determined based on the movement vector VD10 of the user U1 and the index information IND11, as a starting point.

  Here, the neighboring object set N (G, y) is a set of neighboring objects that are related by the edge given to the node y. “G” may be predetermined graph data (eg, graph information GR11). For example, the information processing apparatus 100 performs k neighborhood search processing.

  For example, the information processing apparatus 100 sets the radius r of the hypersphere to ∞ (infinite) (step S300), and extracts the subset S from the existing object set (step S301). For example, the information processing apparatus 100 may extract the object (node) selected as the root node (starting vector) as the subset S. In the example of FIG. 1, the information processing apparatus 100 may extract the starting point vector such as the node N2 as a subset S. Further, for example, a hypersphere is a virtual sphere that indicates a search range. It should be noted that the objects included in the object set S extracted in step S301 are also included in the initial set of the search result object set R at the same time.

  Next, the information processing apparatus 100 extracts, from the objects included in the object set S, an object having the shortest distance from the object y when the search query object is y, and sets it as an object s (step S302). In the example of FIG. 1, the information processing apparatus 100 extracts an object having the shortest distance from the vector VD10 that is a search query object among the objects included in the object set S, and sets it as an object s. For example, the information processing apparatus 100 extracts the node N2 having the shortest distance from the vector VD10 among the objects included in the object set S and sets it as the object s. For example, when only the object (node) selected as the root node (starting vector) is an element of the object set S, the information processing apparatus 100 extracts the root node (starting vector) as the object s as a result. . Next, the information processing apparatus 100 excludes the object s from the object set S (step S303).

  Next, the information processing apparatus 100 determines whether or not the distance d (s, y) between the object s and the object y exceeds r (1 + ε) (step S304). Here, ε is an expansion element, and r (1 + ε) is a value indicating the radius of the search range (search only for nodes within this range. The accuracy can be increased by making it larger than the search range). is there. When the distance d (s, y) between the object s and the object y exceeds r (1 + ε) (step S304: Yes), the information processing apparatus 100 outputs the object set R as a neighborhood object set of the object y (step S304). S305), the process is terminated.

  When the distance d (s, y) between the object s and the search query object y does not exceed r (1 + ε) (step S304: No), the information processing apparatus 100 determines the neighborhood object set N (G, s) of the object s. One object that is not included in the object set C is selected from the objects that are the elements of the selected element u, and the selected object u is stored in the object set C (step S306). The object set C is provided for convenience in order to avoid duplicate search, and is set to an empty set at the start of processing.

  Next, the information processing apparatus 100 determines whether or not the distance d (u, y) between the object u and the object y is equal to or less than r (1 + ε) (step S307). When the distance d (u, y) between the object u and the object y is equal to or less than r (1 + ε) (step S307: Yes), the information processing apparatus 100 adds the object u to the object set S (step S308).

  Next, the information processing apparatus 100 determines whether the distance d (u, y) between the object u and the object y is equal to or smaller than r (step S309). When the distance d (u, y) between the object u and the object y exceeds r (step S309: No), the information processing apparatus 100 performs the determination (processing) in step S315.

  When the distance d (u, y) between the object u and the object y is equal to or less than r (step S309: Yes), the information processing apparatus 100 adds the object u to the object set R (step S310). Then, the information processing apparatus 100 determines whether or not the number of objects included in the object set R exceeds ks (step S311). The predetermined number ks is a natural number that is arbitrarily determined. For example, various settings such as ks = 2 and ks = 10 may be used.

  When the number of objects included in the object set R exceeds ks (step S311: Yes), the information processing apparatus 100 determines an object having the longest (far) object from the object y among the objects included in the object set R. Exclude from the object set R (step S312).

  Next, the information processing apparatus 100 determines whether or not the number of objects included in the object set R matches ks (step S313). When the number of objects included in the object set R matches ks (step S313: Yes), the information processing apparatus 100 determines that the object having the longest (far) distance from the object y among the objects included in the object set R is The distance from the object y is set to a new r (step S314).

  Then, the information processing apparatus 100 determines whether all objects have been selected from the objects that are elements of the neighboring object set N (G, s) of the object s and stored in the object set C (step S315). . When all the objects have not been selected and stored in the object set C from the objects that are elements of the neighboring object set N (G, s) of the object s (step S315: No), the information processing apparatus 100 performs step S306. Return to and repeat the process.

  When all the objects are selected from the objects that are elements of the neighboring object set N (G, s) of the object s and stored in the object set C (step S315: Yes), the information processing apparatus 100 determines that the object set S It is determined whether or not is an empty set (step S316). When the object set S is not an empty set (step S316: No), the information processing apparatus 100 returns to step S302 and repeats the process. If the object set S is an empty set (step S316: Yes), the information processing apparatus 100 outputs the object set R and ends the process (step S317). For example, the information processing apparatus 100 may provide an object (node) included in the object set R as a search result corresponding to the search query (input object y) to the terminal device 10 that performed the search. In the example of FIG. 1, the information processing apparatus 100 searches the nodes N2 and N1 included in the object set R as search results corresponding to the search query (movement vector VD10 in the route RT10 of the user U1). You may provide to the apparatus 10 grade | etc.,. For example, the information processing apparatus 100 provides the travel route MP2 corresponding to the node N2 or the travel route MP35 corresponding to the node N1 to the terminal device 10 used by the user U1 as a travel route similar to the movement of the user U1.

[6. effect〕
As described above, the information processing apparatus 100 according to the embodiment includes the acquisition unit 131, the extraction unit 134, and the transmission unit 135. The acquisition unit 131 is a graph in which a plurality of nodes corresponding to each of a plurality of movement paths and a plurality of nodes associated with corresponding users who have moved corresponding to the respective movement paths are connected according to similarity. Information and movement information related to movement of one user are acquired. The extraction unit 134 searches for graph information starting from a starting node that is a starting point for searching for graph information determined based on a predetermined criterion among a plurality of nodes of the graph information acquired by the acquiring unit 131. Thus, a similar movement route that is a movement route similar to the movement of one user is extracted from the plurality of movement routes. The transmission unit 135 transmits the recommended information related to the movement of the one user to the terminal device used by the one user based on the corresponding user information associated with the similar movement route extracted by the extraction unit 134. .

  As described above, the information processing apparatus 100 according to the embodiment searches the graph information using the starting node as a starting point, and extracts a similar moving path similar to the movement of one user from the plurality of moving paths. Then, the information processing apparatus 100 transmits recommended information related to the movement of the one user to the terminal device used by the one user, based on the information of the corresponding user associated with the extracted similar movement route. Appropriate recommendations can be made according to the movement of the user.

  Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 is configured such that a plurality of nodes associated with visit points visited by a corresponding user in a movement corresponding to each of a plurality of movement paths are similar to a plurality of movement paths. Get connected graph information according to gender. The transmission unit 135 transmits recommended information for recommending a visited place in the movement of one user based on the visited place of the corresponding user associated with the similar movement route.

  As described above, the information processing apparatus 100 according to the embodiment transmits recommended information that recommends a point visited in the movement of one user based on the visit point of the corresponding user associated with the similar movement route. Appropriate recommendations can be made according to the movement of the user.

  Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 includes one piece of graph information in which a plurality of nodes associated with attribute information of a corresponding user are linked according to the similarity of a plurality of travel routes, Get user attribute information. The transmission unit 135 transmits recommendation information based on a movement route in which the attribute information of the corresponding user associated with the similar movement route among the similar movement routes corresponds to the attribute information of one user.

  As described above, the information processing apparatus 100 according to the embodiment has recommended information based on a movement route in which the attribute information of the corresponding user associated with the similar movement route among the similar movement routes corresponds to the attribute information of one user. By transmitting this, it is possible to make an appropriate recommendation according to the movement of the user.

  Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 is a graph in which a plurality of nodes associated with context information in movement corresponding to each movement route are connected according to the similarity of the plurality of movement routes. Information and context information in the movement of one user are acquired. The transmission unit 135 transmits recommendation information based on a movement route in which context information associated with the similar movement route among the similar movement routes corresponds to the context information in the movement of one user.

  As described above, the information processing apparatus 100 according to the embodiment provides the recommended information based on the movement path corresponding to the context information in the movement of one user in the context information associated with the similar movement path among the similar movement paths. By transmitting, an appropriate recommendation can be made according to the movement of the user.

  In the information processing apparatus 100 according to the embodiment, the acquisition unit 131 is a graph in which a plurality of nodes associated with time information in movement corresponding to each movement route are connected according to the similarity of the plurality of movement routes. Information and time information in the movement of one user are acquired. The transmission unit 135 transmits the recommended information based on the movement route in which the time information associated with the similar movement route among the similar movement routes corresponds to the time information in the movement of one user.

  As described above, the information processing apparatus 100 according to the embodiment includes the recommended information based on the movement route corresponding to the time information in the movement of one user in the time information associated with the similar movement route among the similar movement routes. By transmitting, an appropriate recommendation can be made according to the movement of the user.

  In the information processing apparatus 100 according to the embodiment, the acquisition unit 131 is a graph in which a plurality of nodes associated with weather information in movement corresponding to each movement route are connected according to the similarity of the plurality of movement routes. Information and weather information in the movement of one user are acquired. The transmission unit 135 transmits the recommended information based on the travel route in which the weather information associated with the similar travel route among the similar travel routes corresponds to the weather information in the movement of one user.

  As described above, the information processing apparatus 100 according to the embodiment provides the recommended information based on the travel route corresponding to the weather information in the movement of one user in which the weather information associated with the similar travel route among the similar travel routes. By transmitting, an appropriate recommendation can be made according to the movement of the user.

  Further, the information processing apparatus 100 according to the embodiment includes a determination unit 133. The determining unit 133 determines the starting node based on the index information used for determining the starting node. The extraction unit 134 extracts a similar movement route using the starting node determined by the determination unit 133 as a starting point.

  As described above, the information processing apparatus 100 according to the embodiment can search the graph information from an appropriate starting point by determining the starting point node based on the index information used for determining the starting point node. Appropriate recommendations can be made according to travel.

  Further, in the information processing apparatus 100 according to the embodiment, the determination unit 133 determines a starting node based on tree structure type index information.

  As described above, the information processing apparatus 100 according to the embodiment searches the graph information from an appropriate starting point while determining the starting point at high speed by determining the starting point node based on the tree structure type index information. Therefore, an appropriate recommendation can be made according to the movement of the user.

  In the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires graph information in which a plurality of nodes are connected according to the similarity of a plurality of vectors corresponding to each of a plurality of movement paths.

  As described above, the information processing apparatus 100 according to the embodiment acquires the graph information in which a plurality of nodes are connected according to the similarity of a plurality of vectors corresponding to each of the plurality of movement paths, thereby Appropriate recommendations can be made according to travel.

  Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires graph information in which a plurality of vectors indicating features of a plurality of movement paths are connected according to similarity.

  As described above, the information processing apparatus 100 according to the embodiment obtains graph information in which a plurality of vectors indicating the characteristics of a plurality of movement paths are connected according to similarity, and accordingly, according to the movement of the user. Appropriate recommendations can be made.

  Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 connects a plurality of vectors whose elements are feature amounts extracted from each of a plurality of movement paths using a predetermined model according to similarity. Get the graph information.

  As described above, the information processing apparatus 100 according to the embodiment is a graph in which a plurality of vectors whose elements are feature amounts extracted from each of a plurality of movement paths using a predetermined model are connected according to similarity. By acquiring the information, it is possible to make an appropriate recommendation according to the movement of the user.

  Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 inputs a plurality of movement routes into a predetermined model, and thereby obtains a plurality of elements having each feature amount of the plurality of movement routes extracted as an element. The graph information in which the vectors are connected according to the similarity is acquired.

  As described above, the information processing apparatus 100 according to the embodiment inputs information related to a plurality of movement routes to a predetermined model, so that a plurality of vectors whose elements are the feature amounts of the plurality of movement routes to be extracted are obtained. By obtaining the graph information linked according to the similarity, it is possible to make an appropriate recommendation according to the movement of the user.

[7. Hardware configuration)
The information processing apparatus 100 according to the embodiment described above is realized by a computer 1000 configured as shown in FIG. 15, for example. FIG. 15 is a hardware configuration diagram illustrating an example of a computer that implements the functions of the information processing apparatus. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, an HDD (Hard Disk Drive) 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F). ) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400 and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started up, a program depending on the hardware of the computer 1000, and the like.

  The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. The communication interface 1500 receives data from other devices via the network N and sends the data to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the network N.

  The CPU 1100 controls an output device such as a display and a printer and an input device such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. In addition, the CPU 1100 outputs the generated data to the output device via the input / output interface 1600.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides it to the CPU 1100 via the RAM 1200. The CPU 1100 loads such a program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the information processing apparatus 100 according to the embodiment, the CPU 1100 of the computer 1000 executes a program or data (for example, model M1 (model data MDT1)) loaded on the RAM 1200. The function of the control unit 130 is realized. The CPU 1100 of the computer 1000 reads and executes these programs or data (for example, model M1 (model data MDT1)) from the recording medium 1800. As another example, these programs or data are transmitted from other devices via the network N. May be obtained.

  As described above, some of the embodiments of the present application have been described in detail with reference to the drawings. It is possible to implement the present invention in other forms with improvements.

[8. Others]
In addition, among the processes described in the above embodiment, all or part of the processes described as being automatically performed can be performed manually, or the processes described as being performed manually can be performed. All or a part can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various data and parameters shown in the document and drawings can be arbitrarily changed unless otherwise specified. For example, the various types of information illustrated in each drawing is not limited to the illustrated information.

  Further, each component of each illustrated apparatus is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part thereof may be functionally or physically distributed or arbitrarily distributed in arbitrary units according to various loads or usage conditions. Can be integrated and configured.

  In addition, the processes described in the embodiments described above can be appropriately combined within a range in which the process contents are not contradictory.

  In addition, the “section (module, unit)” described above can be read as “means” or “circuit”. For example, the acquisition unit can be read as acquisition means or an acquisition circuit.

DESCRIPTION OF SYMBOLS 1 Information processing system 100 Information processing apparatus 123 Movement path information storage part 124 Index information storage part 125 Graph information storage part 126 Model information storage part 130 Control part 131 Acquisition part 132 Generation part 133 Determination part 134 Extraction part 135 Transmission part 10 Terminal apparatus 50 Information provider N Network

Claims (14)

A plurality of nodes corresponding to each of the plurality of movement paths, and a plurality of nodes associated with corresponding users who have moved corresponding to the respective movement paths are connected according to the similarity of the plurality of movement paths. An acquisition unit for acquiring movement information relating to movement of one user,
By searching the graph information starting from a starting node that is a starting point for searching for the graph information determined based on a predetermined criterion among the plurality of nodes of the graph information acquired by the acquiring unit. An extraction unit for extracting a similar movement route that is a movement route similar to the movement of the one user among the plurality of movement routes;
Transmission that transmits recommended information related to the movement of the one user to the terminal device used by the one user based on the information of the corresponding user associated with the similar movement route extracted by the extraction unit And
An information processing apparatus comprising: The acquisition unit
The plurality of nodes associated with the visit points visited by the corresponding user in the movement corresponding to each of the plurality of movement paths acquire the graph information connected according to the similarity of the plurality of movement paths. ,
The transmitter is
2. The information according to claim 1, wherein the recommendation information for recommending a point visited in the movement of the one user is transmitted based on the visited point of the corresponding user associated with the similar movement route. Processing equipment. The acquisition unit
The plurality of nodes associated with the attribute information of the corresponding user acquire the graph information connected according to the similarity of the plurality of travel routes, and the attribute information of the one user,
The transmitter is
The attribute information of the corresponding user associated with the similar movement route among the similar movement routes transmits the recommended information based on the movement route corresponding to the attribute information of the one user. The information processing apparatus according to claim 1 or 2. The acquisition unit
The graph information in which the plurality of nodes associated with the context information in the movement corresponding to each movement path are connected according to the similarity of the plurality of movement paths, and the context information in the movement of the one user. Acquired,
The transmitter is
The context information associated with the similar movement path among the similar movement paths transmits the recommended information based on the movement path corresponding to the context information in the movement of the one user. The information processing apparatus according to any one of? The acquisition unit
The graph information in which the plurality of nodes associated with the time information in the movement corresponding to each movement route are linked according to the similarity of the plurality of movement routes, and the time information in the movement of the one user. Acquired,
The transmitter is
The time information associated with the similar movement route among the similar movement routes transmits the recommended information based on the movement route corresponding to the time information in the movement of the one user. Information processing apparatus of any one of -4. The acquisition unit
The graph information in which the plurality of nodes associated with the weather information in the movement corresponding to each movement path are connected according to the similarity of the plurality of movement paths, and the weather information in the movement of the one user. Acquired,
The transmitter is
The weather information associated with the similar travel route among the similar travel routes transmits the recommended information based on the travel route corresponding to the weather information in the movement of the one user. Information processing apparatus of any one of -5. A determination unit that determines the starting node based on index information used to determine the starting node;
Further comprising
The extraction unit includes:
The information processing apparatus according to claim 1, wherein the similar movement route is extracted using the starting node determined by the determining unit as a starting point. The determination unit
The information processing apparatus according to claim 7, wherein the starting node is determined based on the tree structure type index information. The acquisition unit
The graph information in which the plurality of nodes are connected is acquired according to the similarity of a plurality of vectors corresponding to each of the plurality of movement paths. Information processing device. The acquisition unit
The information processing apparatus according to claim 9, wherein the graph information obtained by connecting the plurality of vectors indicating the characteristics of the plurality of movement paths according to similarity is acquired. The acquisition unit
The graph information in which the plurality of vectors each having a feature amount extracted from each of the plurality of movement paths using a predetermined model as elements is acquired according to similarity. The information processing apparatus according to claim 9 or claim 10. The acquisition unit
The graph in which the plurality of vectors each having the feature amount of each of the plurality of movement paths extracted by inputting information on the plurality of movement paths into a predetermined model are connected according to similarity. The information processing apparatus according to claim 9, wherein information is acquired. An information processing method executed by a computer,
A plurality of nodes corresponding to each of the plurality of movement paths, and a plurality of nodes associated with corresponding users who have moved corresponding to the respective movement paths are connected according to the similarity of the plurality of movement paths. The acquisition process of acquiring the movement information related to the movement of the graph information and the movement of one user,
By searching the graph information starting from a starting node that is a starting point of the search of the graph information determined based on a predetermined criterion among the plurality of nodes of the graph information acquired by the acquiring step An extraction step of extracting a similar movement route that is a movement route similar to the movement of the one user among the plurality of movement routes;
Transmission for transmitting recommended information related to the movement of the one user to the terminal device used by the one user based on the information of the corresponding user associated with the similar movement route extracted by the extraction step Process,
An information processing method comprising: A plurality of nodes corresponding to each of the plurality of movement paths, and a plurality of nodes associated with corresponding users who have moved corresponding to the respective movement paths are connected according to the similarity of the plurality of movement paths. Acquisition information for acquiring movement information related to movement of a single user,
By searching for the graph information starting from a starting node that is a starting point for searching for the graph information determined based on a predetermined criterion among the plurality of nodes of the graph information acquired by the acquiring procedure An extraction procedure for extracting a similar movement route that is a movement route similar to the movement of the one user among the plurality of movement routes;
Transmission for transmitting recommended information related to the movement of the one user to the terminal device used by the one user based on the information of the corresponding user associated with the similar movement route extracted by the extraction procedure Procedure and
An information processing program for causing a computer to execute.

Images