JP6856567B2 - Information processing equipment, information processing methods, and information processing programs - Google Patents

Description

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

Conventionally, techniques for extracting various types of information have been provided. For example, a technique for estimating the similarity between concepts is provided in consideration of the relative relationships between concepts belonging to other categories.

Japanese Unexamined Patent Publication No. 2007-21351

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

However, with the above-mentioned prior art, it may be difficult to appropriately extract similar contents. For example, it is difficult to extract desired information only by estimating the similarity between concepts belonging to other categories. For example, it is not always possible to estimate the similarity of information belonging to the same category, and in such a case, it is difficult to extract desired information.

The present application has been made in view of the above, and an object of the present application is to provide an information processing device, an information processing method, and an information processing program for appropriately extracting similar contents.

The information processing device according to the present application includes a graph information in which a plurality of nodes corresponding to each of the plurality of contents are connected according to the similarity of the plurality of contents, and an acquisition unit for acquiring the content information related to one content. The graph information is searched from the plurality of nodes of the graph information acquired by the acquisition unit, starting from the starting node that is the starting point for searching the graph information determined based on a predetermined criterion. The present invention is characterized in that it includes a search unit for extracting similar contents that are similar to the one content among the plurality of contents.

According to one aspect of the embodiment, there is an effect that similar contents can be appropriately extracted.

FIG. 1 is a diagram showing an example of information processing according to an embodiment. FIG. 2 is a diagram showing a configuration example of an information processing system according to an embodiment. FIG. 3 is a diagram showing a configuration example of the information processing device according to the embodiment. FIG. 4 is a diagram showing an example of the content information storage unit according to the embodiment. FIG. 5 is a diagram showing an example of the index information storage unit according to the embodiment. FIG. 6 is a diagram showing an example of a graph information storage unit according to the embodiment. FIG. 7 is a diagram showing an example of a model information storage unit according to the embodiment. FIG. 8 is a flowchart showing an example of information processing according to the embodiment. FIG. 9 is a flowchart showing an example of the generation process according to the embodiment. FIG. 10 is a diagram showing an example of extraction of a feature amount according to an embodiment. FIG. 11 is a flowchart showing an example of a search process using graph information. FIG. 12 is a diagram showing an example of determining the fashion of the content. FIG. 13 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device.

Hereinafter, the information processing apparatus, the information processing method, and the mode for carrying out the information processing program (hereinafter referred to as “the embodiment”) according to the present application will be described in detail with reference to the drawings. The information processing apparatus, information processing method, and information processing program according to the present application are not limited by this embodiment. Further, in each of the following embodiments, the same parts are designated by the same reference numerals, and duplicate description is omitted.

(Embodiment)
[1. Information processing]
An example of information processing according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of information processing according to an embodiment. In FIG. 1, as an example of the content, the information processing device 100 has a graph structure of the content which is an application (hereinafter, also simply referred to as “application”) executed on a predetermined device such as the terminal device 10 (see FIG. 2). The case where the content similar to one content (hereinafter, also referred to as “similar content”) is extracted by searching the graph data (graph information) is shown. The predetermined device may be various computers such as a smartphone and a PC (Personal Computer). Further, the content is not limited to the application, and may be various contents such as video content such as a movie, music content such as music, and article content. In FIG. 1, a case where the information processing apparatus 100 uses graph information in which the contents are graph-structured using vector data (also referred to as “vector information” or simply “vector”) corresponding to each content will be described as an example. ..

The information used by the information processing device 100 is not limited to a vector, and may be any form of information as long as it can express the similarity of each content. For example, the information processing apparatus 100 may use graph information in which the content is graph-structured using predetermined data or values corresponding to each content. For example, the information processing apparatus 100 may use graph information in which the content is graph-structured using a predetermined numerical value (for example, a binary value or a hexadecimal value) generated from each content. For example, instead of the vector, the data may be in any form as long as the distance (similarity) between the data is defined. In the example of FIG. 1, a service is provided in which it is determined whether or not one content is popular based on the extracted similar contents and the information of the determination result is provided. However, if the service is based on the similar contents, Such services may be provided. Details on this point will be described later.

[1-1. About graph information]
Further, as shown in the graph information GR11 in FIG. 1, the information processing apparatus 100 performs information processing using graph data in which each vector (node) is connected by a directed edge. The information processing device 100 may generate the graph information as shown in the graph information GR 11 in FIG. 1, and the information processing device 100 may generate the graph information as shown in the graph information GR 11 in FIG. It may be obtained from another external device such as the providing device 50 (see FIG. 2).

Further, the directed edge here means an edge in which data can be traced in only one direction. In the following, the source traced by the edge, that is, the node that is the start point is referred to as the reference source, and the destination that is traced by the edge, that is, the node that is the end point is referred to. 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". 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 in connecting the nodes. For example, the edge connecting the nodes may be an edge that can be referred to each other. For example, the edges connecting the nodes may all be bidirectional edges.

For example, the edge with the node "A" as the reference source is called the output edge of the node "A". Further, for example, the edge with the node "B" as the reference destination is referred to as the input edge of the node "B". That is, the output edge and the input edge referred to here are differences in which of the two nodes in which the directed edge is connected is regarded as the center, and the one directed edge is regarded as the center. Becomes the output edge and the input edge. That is, the output edge and the input edge are relative concepts, and when one directed edge is regarded as the center of the reference source node, it becomes the output edge, and the reference destination node is regarded as the center. In some cases it becomes the input edge. In the present embodiment, the edge is a directed edge such as an output edge or an input edge. Therefore, in the following, the directed edge may be simply referred to as an “edge”.

For example, the information processing apparatus 100 processes a node corresponding to a huge amount of content in the unit of millions to hundreds of millions, but only a part thereof is shown in the drawing. In the example of FIG. 1, in order to simplify the explanation, eight nodes are illustrated to explain the outline of the process. For example, as shown in the graph information GR11 in FIG. 1, the information processing apparatus 100 acquires graph information including a plurality of nodes (vectors) as shown in the nodes N1, N2, N3, and the like. Further, in the example of FIG. 1, each node in the graph information GR 11 is connected with edges (output edges) from the one having the shortest distance to the nodes to a predetermined number of nodes. For example, the predetermined number may be various values such as 2, 5, 10, 100, etc., depending on the purpose, application, and the like. For example, when the predetermined number is 2, the output edge is connected from the node N1 to the node having the closest distance from the node N1 and the two nodes having the second closest distance. The distance as an index indicating the degree of similarity may be any distance as long as it can be applied as a distance between vectors (N-dimensional vectors), for example, Euclidean distance, Mahalanobis distance, cosine distance, and the like. Various distances may be used.

Further, when "node N * (* is an arbitrary numerical value)" is described in this way, it indicates that the node is a node identified by the node ID "N *". For example, when described as "node N1", the node is a node identified by the node ID "N1".

Further, in the graph information GR11 in FIG. 1, the node N10 is connected to the edge E7, which is a directed edge toward the node N7. That is, the node N10 is connected to the node N7 by the edge E7. When "edge E * (* is an arbitrary numerical value)" is described in this way, 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 edge E7 connected with the node N10 as the reference source and the node N7 as the reference destination makes it possible to trace from the node N10 to the node N7. In this case, the edge E7, which is a directed edge, becomes an output edge when identified with the node N10 as the center, and becomes an input edge when identified with the node N7 as the center. Also, the bidirectional arrows in the graph information GR11 of FIG. 1 indicate that the directed edges from both nodes to the other node are connected. For example, the bidirectional arrow between node N2 and node N451 in the graph information GR11 is a concatenation of two edges, a directed edge from node N2 to node N451 and a directed edge from node N451 to node N2. Indicates that it will be done.

Further, the graph information GR11 in FIG. 1 may be in Euclidean space. Further, the graph information GR 11 shown in FIG. 1 is a conceptual diagram for explaining the distance between each vector, and the graph information GR 11 is a multidimensional space. For example, the graph information GR11 shown in FIG. 1 is shown in a two-dimensional manner for being shown on a plane, but is assumed to be a multidimensional space such as 100 dimensions or 1000 dimensions.

Here, the distance between the vector data indicates the similarity of the contents, and the closer the distance is, the more similar the contents are. In the present embodiment, the distance of each node in the graph information GR11 is defined as the degree of similarity between the corresponding objects. For example, it is assumed that the similarity of the contents corresponding to each node is mapped as the distance between the nodes in the graph information GR11. For example, assume that the similarity between the concepts corresponding to each node is mapped to the distance between each node. Here, in the example shown in FIG. 1, the similarity between the objects having a short distance between the nodes in the graph information GR11 is high, and the similarity between the objects having a long distance between the nodes in the graph information GR11 is low. For example, in the graph information GR11 in FIG. 1, the node identified by the node ID “N35” and the node identified by the node ID “N693” are close to each other, that is, the distance is short. Therefore, it is shown that the object corresponding to the node identified by the node ID "N35" and the object corresponding to the node identified by the node ID "N693" have a high degree of similarity.

Further, for example, in the graph information GR11 in FIG. 1, the node identified by the node ID "N7" and the node identified by the node ID "N2" are remote, that is, the distance is long. Therefore, it is shown that the object corresponding to the node identified by the node ID "N7" and the object corresponding to the node identified by the node ID "N2" have a low degree of similarity.

[1-2. Vector generation example]
Further, each node (vector) referred to here corresponds to each object (content). If the content information is numerical data indicating the state of various contents, it can be treated as it is as vector data. However, if the content information is text, image, or voice, conversion to vector data is required. Therefore, in the example of FIG. 1, the multidimensional (N-dimensional) vector generated by the feature amount extracted from the content information of each content may be an object.

For example, the information processing apparatus 100 may generate an N-dimensional vector from the content information of each content by using a model that extracts features of the content information. In the example of FIG. 1, as shown in the model information storage unit 124 (see FIG. 7), the information processing apparatus 100 uses the model (model M1) identified by the model ID “M1” to provide content information of each content. Generate a vector from. As described above, when "model M * (* is an arbitrary numerical value)" is described, it means that the model is a model identified by the model ID "M *". For example, when described as "model M1", the model is a model identified by the model ID "M1". Further, as shown in the model information storage unit 124, the model M1 is a model used for use "feature extraction (app)", that is, feature extraction from content data (content information), and a specific model thereof. Indicates that the data is "model data MDT1".

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

Here, FIG. 10 is used to show an example of generating vector data corresponding to each content. FIG. 10 is a diagram showing an example of extraction of a feature amount according to an embodiment. FIG. 10 is a conceptual diagram of the model M1. In FIG. 10, the line showing each connection relationship of each element (neuron) is omitted. As shown in FIG. 10, 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 into which content information is input. Further, the output layer OL is a layer that outputs the same information as the input content information in response to the input to the input layer IL.

Further, for example, the most compressed compression layer RP in the central portion of the intermediate layer CL is a layer expressing the characteristics of the input content information. 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 the portion where the encoding process is performed. In the intermediate layer CL of the model M1, between the input layer IL and the compression layer RP corresponds to a portion that performs a process of compressing the features of the input content information. 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 the portion where the decoding process is performed. 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 content information.

For example, the information processing apparatus 100 may use information such as neurons NL1 and neurons NL2 included in the compression layer RP as a vector. For example, the information processing apparatus 100 uses the value VE1 corresponding to the calculated neuron NL1 and the value VE2 corresponding to the neuron NL2 as vector elements (values of one dimension) when the content information of a certain content is input. It may be extracted. For example, the information processing apparatus 100 may extract the value VE1 corresponding to the calculated neuron NL1 as the first-dimensional element of the vector of the content when the content information of a certain content is input. Further, for example, the information processing apparatus 100 may extract the value VE2 corresponding to the calculated neuron NL2 as the second-dimensional element of the vector of the content when the content information of a certain content is input. In this way, the information processing apparatus 100 may generate a vector corresponding to each content by inputting the content information of each content into the model M1. The information processing device 100 may acquire a vector corresponding to each content from another external device such as the information providing device 50. The information processing apparatus 100 may use the value itself corresponding to each neuron as each element of the vector, or may use a value obtained by multiplying the value corresponding to each neuron by a predetermined coefficient. Further, in the example of FIG. 1, for the sake of simplicity, the case where each element (value) of the vector is an integer is shown, but each element (value) of the vector is a real number including a numerical value after the decimal point. May be good.

The information processing apparatus 100 is not limited to the elements (neurons) of the compression layer RP, and may use information of other elements (neurons) in the intermediate layer CL as a vector. For example, the information processing apparatus 100 may use information such as the neuron NL3 in the encoding portion and the neuron NL4 in the decoding portion as a vector. For example, the information processing apparatus 100 uses the value VE3 corresponding to the calculated neuron NL3 and the value VE4 corresponding to the neuron NL4 as vector elements (values of one dimension) when the content information of a certain content is input. It may be extracted. The above is an example, and the information processing apparatus 100 is not limited to the autoencoder, and various models may be used to extract features from the content information. For example, a model trained to discriminate between content suitable for a predetermined purpose such as a startup member and content unsuitable may be generated, and the intermediate layer thereof may be extracted as vector data. The model may also be generated by, for example, a method of learning similarity such as triplet loss. Further, the information processing apparatus 100 may perform feature extraction without using a model. For example, the information processing device 100 may be set by the administrator of the information processing device 100 or the like to extract information corresponding to features (identities) from the content information and generate a vector. For example, the information processing apparatus 100 may generate a vector by extracting information corresponding to a feature (feature) such as a content genre (category) or content from the content information.

Further, for example, the information processing device 100 may acquire the model M1 from another external device such as the information providing device 50. The information processing device 100 may generate the model M1 by inputting the content information CDT1, CDT2, CDT451, etc. of each content stored in the content information storage unit 121 (see FIG. 4). For example, the information processing apparatus 100 may generate the model M1 by inputting the content information CDT1, CDT2, CDT451, etc. of each content. For example, the content information CDT1, CDT2, CDT451 and the like may include the genre (category) and content of the content. For example, the content information such as CDT1, CDT2, CDT451, etc., when the content is an application, includes information indicating categories such as "weather", "route", and "game", and image information indicating a screen at startup. , Various information such as information indicating the billing amount may be included. For example, the content information such as the content information CDT1, CDT2, and CDT451 may include information such as character information and image information corresponding to the article content when the content is the article content. Further, for example, the content information such as the content information CDT1, CDT2, and CDT451 may include information such as image information and audio information corresponding to the moving image content when the content is moving image content. When the content is an application, the content information may be only the source code (character information) thereof.

The information processing apparatus 100 includes an input layer into which content information of contents is input, an output layer, a first element which is any layer from the input layer to the output layer and belongs to a layer other than the output layer, and a first element. With respect to the content information input to the input layer, including the second element whose value is calculated based on the element and the weight of the first element, each element belonging to each layer other than the output layer is set as the first element. By performing the calculation 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. The information processing device 100 may acquire a model used for vector generation from another external device such as the information providing device 50.

[1-3. Processing example]
From here, the case where the information processing apparatus 100 acquires the content information of one content from the user and provides the service based on the content similar to the one content will be described as an example. In the example of FIG. 1, the terminal device 10 is used by the user U1 who belongs to a predetermined software development company. For example, the user U1 may be a user who develops an application at a company that develops an application for a smartphone. The example of FIG. 1 shows a case where information on whether or not content X, which is an application developed by the user U1 who is a developer of an application for a smartphone, is popular is desired.

First, the terminal device 10 transmits the content information CDT 11 of the content X to the information processing device 100. In the example of FIG. 1, the content information CDT11 and the like include information on the genre (category) and the content of the content (app). The content information CDT11 and the like may be only the source code (character information) of the content (app).

Then, the information processing device 100 acquires the content information related to one content (step S11). In the example of FIG. 1, the information processing device 100 acquires the content information CDT11 related to the content X from the terminal device 10 of the user U1. The information processing device 100 acquires the content information CDT 11 related to the content X from the terminal device 10 (see FIG. 2) carried by the content X.

Then, the information processing apparatus 100 generates a vector used for searching graph information from the content information corresponding to one content. In the example of FIG. 1, the information processing apparatus 100 generates a vector corresponding to the content X by the processing as shown in the processing group PS11. The information processing device 100 inputs the content information CDT11 regarding the content X into the model M1 (step S12). Specifically, the information processing apparatus 100 inputs the content information CDT11 of the content X into the model M1. Then, the information processing apparatus 100 generates a vector by using the information in the model M1 after the input of the content information CDT11 (step S13). For example, the information processing apparatus 100 generates vector data using each element in the model M1 in which the content information CDT11 is input.

In the example of FIG. 1, the information processing apparatus 100 generates vector data VD11 (also simply referred to as “vector VD11”) by using the values of each element in the model M1 in which the content information CDT11 is input. For example, the information processing apparatus 100 sets a value VE1 (see FIG. 10) corresponding to the neuron NL1 of the model M1 and a value VE2 (see FIG. 10) corresponding to the neuron NL2 when the content information CDT11 of the content X is input. To generate a vector. For example, the information processing apparatus 100 may extract the value VE1 corresponding to the calculated neuron NL1 as the first-dimensional element of the vector VD11 when the content information CDT11 of the content X is input. Further, for example, the information processing apparatus 100 generates the vector VD11 by using the value VE2 corresponding to the calculated neuron NL2 as the second-dimensional element of the vector VD11 when the content information of a certain content is input. In the example of FIG. 1, the information processing apparatus 100 generates a vector VD11 in which the element of the first dimension is "35" and the element of the second dimension is "63".

Then, the information processing apparatus 100 searches for content similar to the content X (hereinafter, also referred to as “similar content”) (step S14). For example, the information processing apparatus 100 may search for similar contents of the content X by appropriately using various conventional techniques such as a neighborhood search technique disclosed in Non-Patent Document 1. For example, the information processing apparatus 100 uses the graph information GR11 in which nodes having high similarity are connected by an edge.

In the example of FIG. 1, as shown in the information group INF11, the information processing apparatus 100 searches for similar contents of the content X by using the graph information GR11 and the index information IND11. For example, the information processing apparatus 100 acquires the graph information GR11 regarding the content from the graph information storage unit 123 (see FIG. 6). Further, for example, the information processing apparatus 100 uses the index information storage unit 122 (see FIG. 5) to obtain the index information IND11 used for determining the node (hereinafter, also referred to as “starting point vector”) that is the starting point of the search in the graph information GR11. get. The index information IND 11 may be generated by the information processing device 100, or the information processing device 100 may acquire the index information IND 11 from another external device such as the information providing device 50.

Then, the information processing apparatus 100 uses the index information IND11 in order to determine (specify) the starting point vector corresponding to one content (query). In the example of FIG. 1, the information processing apparatus 100 uses the index information IND11 to determine (specify) the starting point vector corresponding to the vector VD11 of the content X. That is, the information processing apparatus 100 uses the vector VD11 and the index information IND11 to determine the starting point vector in the graph information GR11.

The index information IND11 in FIG. 1 has a hierarchical structure shown in the index information storage unit 122 in FIG. For example, the index information IND11 indicates that the nodes (vectors) of the first layer located immediately below the root RT are nodes VT1, VT2, and the like. Further, for example, the index information IND11 indicates that the node of the second layer immediately below the node VT2 is the node VT2-1 to VT2-4 (not shown). Further, for example, the index information IND11 indicates that the node of the third layer immediately below the node VT2-2 is a node N35, a node N451, and a node N693, that is, a node (vector) in the graph information GR11.

For example, the information processing apparatus 100 determines the starting point vector in the graph information GR 11 by using the tree-structured index information as shown in the index information IND 11 in FIG. 1 (step S15). In the example of FIG. 1, the information processing apparatus 100 efficiently generates the vector VD11 and then traces the index information IND11 from top to bottom to identify a starting point vector that is a candidate near the index information IND11. The origin vector corresponding to the search query (one content) can be determined.

For example, the information processing apparatus 100 may determine the starting point vector corresponding to the vector VD11 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. 1, for example, the information processing apparatus 100 determines the node N451 as a starting point vector by tracing the index information IND11 from the root RT to the node N451. For example, the information processing apparatus 100 may determine the leaf node reached as a starting point 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 the index information IND11 downward based on the degree of similarity with the vector VD11. For example, the information processing apparatus 100 may determine which node, such as node VT1 or VT2, is to be traced from the root RT based on the degree of similarity between the vector VD11 and the nodes VT1 and VT2. For example, the information processing apparatus 100 may determine that the root RT traces to the node VT2 having the highest degree of similarity to the vector VD11 among the nodes VT1, VT2, and the like. Further, for example, the information processing apparatus 100 may determine from the node VT2 to the node VT2-2 having the highest degree of similarity to the vector VD11 among the nodes VT2-1 to VT2-4 and the like. Further, for example, the information processing apparatus 100 may determine that the node VT2-2 traces to the node node N451 having the highest similarity to the vector VD11 among the nodes N35, N451, N693 and the like. In the example of FIG. 1, for the sake of simplicity, the 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 nodes N451, N35, N693, and N2 as starting point vectors. It should be noted that one or more nodes may be randomly selected from the graph information GR11 at the start of the search without using the index information IND11 and used as a starting point vector, or one or more nodes specified in advance may be used as the starting point. It may be a vector.

Then, the information processing apparatus 100 extracts similar contents of the contents X by searching the graph information GR11 (step S16). For example, the information processing device 100 extracts a node located in the vicinity of the node N451 as similar content. For example, the information processing device 100 extracts nodes that are close to the node N451 as similar contents. For example, the information processing apparatus 100 extracts a node that can be reached from the node N451 as similar content by tracing the edge starting from the node N451. For example, the information processing device 100 extracts a predetermined number of nodes (for example, 2 or 10 nodes) as similar contents. For example, the information processing apparatus 100 may extract similar contents of the contents X by a search process as shown in FIG. 11, but the details will be described later. In the example of FIG. 1, the information processing apparatus 100 extracts the node N451 and the node N35 as similar contents by searching the graph information GR11 starting from the node N451.

Then, the information processing device 100 acquires information regarding index values of similar contents. In the example of FIG. 1, the information processing apparatus 100 acquires an index value of similar content from the content information storage unit 121 (see FIG. 4). The index value of the content referred to here may be various index values such as sales of the content and the number of postings by the user regarding the content as long as it is information used for determining whether or not the content is popular. For example, when the content is an application, the number of downloads, the number of users, and the like may be used as index values. Further, the information processing apparatus 100 may use a plurality of index values.

In the example of FIG. 1, it is assumed that the sales of the content (app) are used as the index value of the content. In the example of FIG. 1, the information processing apparatus 100 acquires information such as the index value VL451 of the node N451 (content # 451) and the index value VL35 of the node N35 (content # 35) as shown in the similar information SLT. For example, the index value VL451 indicates the sales of the content # 451 and the index value VL35 indicates the sales of the content # 35. For example, the index value VL451 and the index value VL35 are assumed to be specific numerical values such as 2 million yen and 100 million yen.

Then, the information processing apparatus 100 determines the index value (determination index value) used for the determination (step S17). In the example of FIG. 1, the information processing apparatus 100 determines based on information such as the index value VL451 of the node N451 (content # 451) and the index value VL35 of the node N35 (content # 35) as shown in the similar information SLT. Determine the index value for use.

For example, the information processing apparatus 100 determines the average of the evaluation values (index values) of the similar contents included in the similar information SLT as the determination index value. In the example of FIG. 1, the information processing apparatus 100 determines the average of the index values of the similar contents extracted in step S16 as the index value for determination. That is, the information processing apparatus 100 determines the average of the index value VL451 of the content # 45 and the index value VL35 of the content # 35 included in the similar information SLT as the index value for determination. In this case, the information processing apparatus 100 calculates the total value of the index values of similar contents by adding the index value VL451 of the content # 45 and the index value VL35 of the content # 35. Then, the information processing apparatus 100 calculates the determination index value by dividing the total value of the index values of the similar contents by the number of similar contents. For example, when the information processing apparatus 100 has two similar contents, the content # 45 and the content # 35, the information processing apparatus 100 divides the total value obtained by adding the index value VL451 and the index value VL35 by the number of similar contents "2". As a result, the index value for judgment is calculated. In the example of FIG. 1, the information processing apparatus 100 determines the determination index value DVL11 as shown in the determination information VLT.

The above is an example, and the information processing apparatus 100 may determine any evaluation value (index value) as the determination index value based on the index value of the similar content included in the similar information SLT. For example, the information processing apparatus 100 may determine the highest index value among the similar information SLTs as the determination index value. For example, the information processing apparatus 100 may determine the lowest index value among the similar information SLTs as the determination index value. For example, the information processing apparatus 100 may determine the index value of the content most similar to the content X among the similar information SLTs as the index value for determination.

Then, the information processing apparatus 100 performs a determination process using the determination index value (step S18). For example, the information processing apparatus 100 determines whether or not the content X is popular by comparing the determination index value with a predetermined threshold value. For example, the information processing apparatus 100 determines that the content X is prevalent when the determination index value is equal to or higher than a predetermined threshold value. For example, the information processing apparatus 100 determines that the content X does not become popular when the determination index value is less than a predetermined threshold value. In the example of FIG. 1, the information processing apparatus 100 performs the determination process using the threshold value TH11 as shown in the threshold value information TLT. For example, the threshold value TH11 is assumed to be a specific numerical value such as 5 million yen or 100 million yen. The information processing device 100 may acquire the threshold value TH11 from the storage unit 120 (see FIG. 3) that stores the threshold value TH11, or may acquire the threshold value TH11 from an external information processing device. For example, when the information processing apparatus 100 acquires the threshold value TH11 from the storage unit 120, the threshold value TH11 is stored in the storage unit 120. In the following description, it is assumed that the threshold value TH11 is "10 million yen" and the determination index value DVL11 is "30 million yen".

As described above, since the threshold value TH11 is "10 million yen" and the determination index value DVL11 is "30 million yen", the information processing apparatus 100 has the determination index value DVL11 and the predetermined threshold value TH11. Is compared, and it is determined that the determination index value DVL11 is equal to or higher than the predetermined threshold value TH11. In this case, since the information processing apparatus 100 satisfies the criterion that the determination index value DVL11 is equal to or higher than the predetermined threshold value TH11, the information processing apparatus 100 determines that the content X is in fashion as shown in the fashion determination result DRS.

Then, the information processing device 100 provides a service based on the determination result. In the example of FIG. 1, the information processing device 100 provides the terminal device 10 with information (trend determination information) indicating that the content X is a content (trend content) that may be trendy (step S19). For example, the information processing device 100 transmits trend determination information indicating that the content X is trendy content to the terminal device 10. As a result, the user U1 can determine that the content X may become popular.

On the other hand, when the information processing apparatus 100 determines that the determination index value DVL11 is less than the predetermined threshold value TH11, the information processing apparatus 100 determines that the content X is not popular. Then, the information processing device 100 transmits trend determination information indicating that the content X is unlikely to be trendy to the terminal device 10. As a result, the user U1 can determine that there is a high possibility that the content X will not be popular, and can develop the popular content by changing the specifications of the content X or the like.

As described above, the information processing apparatus 100 uses the graph information GR11 and the index information IND11 to extract similar contents of the content X acquired from the user U1. For example, the information processing apparatus 100 uses the index information IND11 to determine the starting point vector in the graph information GR11 when extracting similar contents of the content X. Then, the information processing apparatus 100 extracts similar contents of the content X by searching the graph information GR 11 with the determined starting point vector as the starting point. As a result, the information processing apparatus 100 can appropriately extract similar contents.

For example, the information processing apparatus 100 uses graph information to provide similar content to the content X even when the content (similar content) similar to the content X does not seem to have a relationship with the content X at first glance. It can be extracted, and similar contents can be appropriately extracted. For example, there is a method of identifying content suitable for a predetermined purpose such as an XX game or YY article content by a method such as deep learning, but there are various factors that affect whether or not the content is suitable for a predetermined purpose. In some cases, since there are few cases of each element, there is a problem that it is difficult to improve the identification accuracy because learning is not possible. For example, there is a method of inputting content information into the model using a model generated by a learning method such as deep learning and identifying whether the content is suitable for a predetermined purpose from the output score, but there are various methods. When there are various elements, it is difficult to improve the accuracy of the model and it is difficult to improve the identification accuracy because there are few cases of each element. On the other hand, the information processing apparatus 100 performs a search using graph-structured graph information of a plurality of vectors generated based on the features extracted from each content, and compares the vectors of the contents to determine the degree of similarity between the contents. Is determined, and it can be appropriately determined whether or not one content is included in a plurality of contents. Therefore, the information processing apparatus 100 can appropriately extract similar contents.

[1-4. About service]
In the example of FIG. 1, the determination of whether or not the content is popular has been described as an example, but the information processing apparatus 100 may provide various services by using the information of the extracted similar content. This point will be described below.

[1-4-1. Providing information about users]
For example, the information processing device 100 may provide information about a user who uses the content by using the information of the extracted similar content. For example, the information processing apparatus 100 may provide information indicating to what kind of user one content is used by using the information of the extracted similar content.

In the example of FIG. 1, the information processing apparatus 100 uses the information of the user who uses the content # 451 and the content # 35 which are similar contents, and gives the information indicating what kind of user the content X is used by the user. It may be provided to U1. The information processing device 100 uses the information of the user who uses the content # 451 and the content # 35, which are similar contents, to give the user U1 information (analysis information) indicating what kind of user the content X is used by. May be provided. For example, the information processing device 100 may provide the user U1 with attribute information of a user who uses the content # 451 or the content # 35. For example, when the information processing device 100 has many male users in their twenties who use the content # 451 and the content # 35, the information processing device 100 provides analysis information indicating that the content X is likely to be used by the male users in their twenties. It may be transmitted to the terminal device 10 of U1.

Further, for example, the information processing device 100 may transmit analysis information indicating the ratio of each attribute among the users who use the content # 451 and the content # 35 to the terminal device 10 of the user U1. For example, in the information processing device 100, when 80% of the users who use the content # 451 and the content # 35 are male and 20% are female, 80% are male and 20% are female. The analysis information indicating that may be transmitted to the terminal device 10 of the user U1.

[1-4-2. Judgment of generated content]
In the example of FIG. 1, the information processing device 100 determines the fashion of the content X acquired from the user U1, but the information processing device 100 may generate the content and determine the fashion of the generated content. For example, the information processing device 100 generates content such as an application, video content, music content, or article content, and extracts similar content of the generated content using graph information to determine the fashion of the generated content. You may. This point will be described with reference to FIG. FIG. 12 is a diagram showing an example of determining the fashion of the content. In the example of FIG. 12, the case where the content is an application is shown. Further, the graph information GR 21 may be the graph information GR 11 in FIG. The same points as in FIG. 1 will be omitted as appropriate.

First, the information processing device 100 generates the content (step S51). The content generation referred to here includes a case where the information processing device 100 itself generates the content and a case where the information processing device 100 requests another external device to generate the content. The information processing device 100 may generate the application Y in its own device, or may request the generation of the application Y from another external device and acquire the application Y from the other external device that generated the application Y. .. For example, the information processing apparatus 100 may randomly generate the application Y by appropriately using a technique related to automatic generation. Further, for example, when the content is music content such as music, the information processing device 100 generates music content by extracting each section such as several seconds or several measures from a plurality of past songs and randomly combining them. You may. For example, the information processing device 100 may be generated by randomly combining sections of a plurality of songs that have been popular in the past. For example, the information processing apparatus 100 may generate by randomly combining a plurality of sections of music that satisfy the conditions related to fashion such as sales of a predetermined threshold value (for example, 1 billion yen or the like) or more. The information processing device 100 is not limited to the above, and may generate the content by any method such as a technique related to automatic generation or random generation as long as the content can be generated. For example, the information processing device 100 may request the information providing device 50 to generate the application Y, and may acquire the application Y from the information providing device 50 that generated the application Y. In the example of FIG. 12, the information processing device 100 generates the application Y.

Then, the information processing apparatus 100 uses the graph information to determine the fashion of the content (step S52). In the example of FIG. 12, the information processing apparatus 100 uses the graph information GR 21 to determine the fashion of the application Y. For example, the information processing apparatus 100 extracts information on content (similar content) similar to the application Y from the graph information GR 21 for the generated content (app Y). For example, the information processing device 100 acquires content information (content information YLD) related to the application Y. For example, the information processing apparatus 100 acquires the graph information GR 21 from the graph information storage unit 123. For example, the information processing apparatus 100 acquires graph information GR21 in which a plurality of nodes corresponding to each of the contents are connected according to the similarity of the plurality of contents. Then, the information processing apparatus 100 generates a vector (vector YVD) from the content information YDT of the application Y by using the model M1 stored in the model information storage unit 124.

Then, the information processing apparatus 100 determines the starting point vector as the node YNE (corresponding to the application AP5 in FIG. 12) by using the generated vector YVD and the index information. Then, the information processing apparatus 100 extracts similar contents (similar groups) of the application Y by searching the graph information GR 21 starting from the node YNE which is the starting point vector. For example, the information processing apparatus 100 extracts a predetermined number (for example, two) of nodes as similar contents by searching the graph information GR 21 starting from the node YNE. In the example of FIG. 12, the information processing apparatus 100 extracts the application AP1 (node NET1) and the application AP5 (node NET5) included in the similar content range SAR11 as similar contents.

Then, the information processing device 100 determines whether or not the application Y is popular by using the index value of the extracted similar content. For example, the information processing apparatus 100 acquires information regarding index values of similar contents. In the example of FIG. 12, the number of downloads of the content (app) is used as the index value of the content. In the example of FIG. 12, the information processing apparatus 100 acquires an index value of similar content from the content information storage unit 121 (see FIG. 4). Then, the information processing apparatus 100 determines the index value (determination index value) used for the determination. In the example of FIG. 12, the information processing device 100 sets the index value VLAP1 of the application having the largest number of downloads (for example, application AP1) among the application AP1 and the application AP5 as the determination index value (hereinafter, “determination index value”). DVL51 ”). The index value VLAP1 is assumed to be a specific numerical value such as 50,000 times or 1 million times.

Then, the information processing device 100 performs the determination process using the determination index value. For example, the information processing apparatus 100 determines whether or not the content X is popular by comparing the determination index value with a predetermined threshold value. In the example of FIG. 12, the information processing apparatus 100 performs the determination process using the threshold value TH51. For example, the threshold value TH11 is assumed to be a specific numerical value such as 250,000 times or 5 million times. The information processing device 100 may acquire the threshold value TH51 from the storage unit 120 (see FIG. 3) that stores the threshold value TH51, or may acquire the threshold value TH51 from an external information processing device.

For example, when the information processing apparatus 100 determines that the determination index value DVL51 is equal to or higher than the predetermined threshold value TH51, the information processing apparatus 100 determines that the content X is prevalent. On the other hand, when the information processing apparatus 100 determines that the determination index value DVL11 is less than the predetermined threshold value TH11, the information processing apparatus 100 determines that the content X is not popular. In this way, the information processing apparatus 100 can appropriately generate the trendy content by determining the trend of the generated content. Further, the information processing apparatus 100 may decide to sell or release the generated content when it is determined that the generated content is popular. Then, the information processing device 100 may provide the generated content to various external devices such as the terminal device 10 and the information providing device 50.

[1-5. Index information]
The index information (index data) shown in the example of FIG. 1 is an example, and the information processing apparatus 100 may search for graph information using various index information. Further, for example, the information processing apparatus 100 may generate index data to be used at the time of search. For example, the information processing apparatus 100 generates a search index for searching a high-dimensional vector as index data. The high-dimensional vector referred to here may be, for example, a vector having several hundred dimensions to several thousand dimensions, or a vector having 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. 1 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 index data having another tree structure. For example, the information processing apparatus 100 may generate various index data in which the leaf of the index data of the tree structure is connected to the graph data. For example, the information processing apparatus 100 may generate various index data in which the leaf of the index data of the tree structure corresponds to the node in the graph data. Further, when the information processing apparatus 100 searches using such index data, the graph data may be searched from the leaf (node) reached by tracing the index data.

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

[2. Information processing system configuration]
As shown in FIG. 2, 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 communicably connected to each other via a predetermined network N by wire or wirelessly. FIG. 2 is a diagram showing a configuration example of an information processing system according to an embodiment. The information processing system 1 shown in FIG. 2 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 the user. The terminal device 10 accepts various operations by the user. In the following, the terminal device 10 may be referred to as a user. That is, in the following, 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. For example, the terminal device 10 may be an information processing device in which an administrator uses a predetermined server system. For example, the terminal device 10 collects the content information and transmits the content information to the information processing device 100.

The information processing device 100 is an information processing device that determines whether or not one content among a plurality of contents is included in the plurality of contents by searching graph information with the starting point vector as the starting point. For example, the information processing apparatus 100 acquires content information related to one content, and determines the starting point vector based on the content information and the information related to the starting point vector that is the starting point for searching the graph information.

The information processing device 100 is an information processing device in which information for providing various information to a user or the like is stored. For example, when the information processing device 100 acquires the content information (hereinafter, also referred to as "query information" or "query") of one content from the terminal device 10, the information processing device 100 searches for the content (vector information or the like) similar to the query. The search result is provided to the terminal device 10. In the example of FIG. 1, when the information processing device 100 acquires the content information of one content from the terminal device 10, it searches for the content similar to the one content and provides the search result to the terminal device 10 as the similar content. .. Further, for example, the data provided by the information processing device 100 to the terminal device 10 may be the name of the content or the content information itself of the content, or for referring to the corresponding data such as a URL (Uniform Resource Locator). It may be information.

The information providing device 50 is an information processing device in which information for providing various information to the information processing device 100 is stored. For example, the information providing device 50 may store content information or the like collected from various external devices such as a web server. For example, the information providing device 50 is an information processing device that provides various information such as graph information, index information, and a model to the information processing device 100.

[3. Information processing device configuration]
Next, the configuration of the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram showing a configuration example of the information processing device according to the embodiment. As shown in FIG. 3, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The information processing device 100 includes an input unit (for example, a keyboard, a mouse, etc.) that receives various operations from the administrator of the information processing device 100, and a display unit (for example, a liquid crystal display, etc.) for displaying various information. You may have.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. Then, the communication unit 110 is connected to the network (for example, the network N in FIG. 2) by wire or wirelessly, and transmits / receives information to / from the terminal device 10 and the information providing device 50.

(Memory unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. As shown in FIG. 3, the storage unit 120 according to the embodiment includes a content information storage unit 121, an index information storage unit 122, a graph information storage unit 123, and a model information storage unit 124. Further, the storage unit 120 may store information such as the threshold value TH11.

(Content information storage unit 121)
The content information storage unit 121 according to the embodiment stores various information related to the content (object). For example, the content information storage unit 121 stores the content ID and vector data. In the example of FIG. 4, content information regarding the content is stored. FIG. 4 is a diagram showing an example of the content information storage unit according to the embodiment. The content information storage unit 121 shown in FIG. 4 includes items such as "content ID", "content", "content information", "index value", and "vector information".

The "content ID" indicates identification information for identifying the content (object). Further, the "content" indicates a specific name, content, or the like of the content identified by the content ID. In the example of FIG. 4, the content is indicated by an abstract code such as "content # 1", but even if character information or the like for specifying the content is included such as "A content" in parentheses. Good. The "index value" indicates a predetermined index value of the content identified by the content ID. For example, the "index value" may be a value indicating the sales of the content when the index is "sales". Further, for example, when the content is an application, the "index value" may be the number of downloads, the number of users, or the like. The "index value" is not limited to the above, and may be any index value as long as it relates to the content. When a plurality of index values are used, the item of "index value" may be the number (plurality) according to the number of index values to be used. In the example of FIG. 4, the index value is indicated by an abstract code such as "VL1", but a specific numerical value corresponding to the index value such as 1000 or 1 million may be stored.

"Content information" indicates information about the content identified by the content ID. In the example of FIG. 4, the content information is indicated by an abstract code such as "CDT1", but each content information is information about various contents such as the genre (category) and the contents of the contents identified by the contents ID. It may be included. The "vector information" indicates vector information corresponding to the content (object) identified by the content ID. That is, in the example of FIG. 4, the vector data (vector information) corresponding to the object is associated and registered with the content ID that identifies the content (object).

For example, in the example of FIG. 4, the content (object) identified by the content ID "CT1" is associated with multidimensional (N-dimensional) vector information of "10, 24, 54, 2 ...". Is shown. For example, for the content # 1, the multidimensional (N-dimensional) vector information of "10, 24, 54, 2 ..." indicating the characteristics of the content # 1 is extracted from the content information CDT1 by the model M1 or the like. Show that. Further, in the example of FIG. 4, the index value of the content # 1 is shown to be “VL1”.

The content information storage unit 121 is not limited to the above, and various information may be stored depending on the purpose.

(Index information storage unit 122)
The index information storage unit 122 according to the embodiment stores various information related to the index. FIG. 5 is a diagram showing an example of the index information storage unit according to the embodiment. Specifically, in the example of FIG. 5, the index information storage unit 122 shows index information having a tree structure. In the example of FIG. 5, the index information storage unit 122 includes items such as "root hierarchy", "first hierarchy", "second hierarchy", and "third hierarchy". In addition, not limited to "first layer" to "third layer", "fourth layer", "fifth layer", "sixth layer" and the like may be included depending on the number of layers of the index.

The "route hierarchy" indicates a hierarchy of routes (top level) that is a starting point for determining a starting node using an index. The "first layer" stores information for identifying (identifying) a node (a node or a vector in graph information) belonging to the first layer of the index. The node stored in the "first layer" is a node corresponding to the layer directly connected to the root of the index.

The "second layer" stores information for identifying (identifying) a node (a node or a vector in graph information) belonging to the second layer of the index. The node stored in the "second layer" is a node corresponding to the immediately lower layer connected to the node of the first layer. The "third layer" stores information for identifying (identifying) a node (a node or a vector in graph information) belonging to the third layer of the index. The node stored in the "third layer" is a node corresponding to the immediately lower layer connected to the node of the second layer.

For example, in the example shown in FIG. 5, the index information storage unit 122 stores information corresponding to the index information IND11 in FIG. For example, the index information storage unit 122 indicates that the nodes of the first layer are nodes VT1 to VT3 and the like. The numerical value in parentheses below each node indicates the value of the vector corresponding to each node.

Further, for example, the index information storage unit 122 indicates that the node of the second layer immediately below the node VT2 is the node VT2-1 to VT2-4. Further, for example, the index information storage unit 122 indicates that the node of the third layer immediately below the node VT2-2 is a node (vector) in the graph information GR11 of the node N35, the node N451, and the node N693.

The index information storage unit 122 is not limited to the above, and various information may be stored depending on the purpose.

(Graph Information Storage Unit 123)
The graph information storage unit 123 according to the embodiment stores various information related to the graph information. FIG. 6 is a diagram showing an example of a graph information storage unit according to the embodiment. In the example of FIG. 6, the graph information storage unit 123 has items such as "node ID", "content ID", and "edge information". Further, the "edge information" includes information such as "edge ID" and "reference destination".

The "node ID" indicates identification information for identifying each node (content) in the graph data. Further, the "content ID" indicates identification information for identifying the content (object).

Further, "edge information" indicates information about the edge connected to the corresponding node. In the example of FIG. 6, “edge information” indicates a case where the edge is a directed edge, and indicates information about an output edge output from the corresponding node. Further, the "edge ID" indicates identification information for identifying an edge connecting the nodes. Further, "reference destination" indicates information indicating a reference destination (node) connected by an edge. That is, in the example of FIG. 6, the reference destination (output edge) to which the information identifying the object (content) corresponding to the node and the directed edge (output edge) from the node are connected to the node ID that identifies the node Node) is associated and registered.

For example, in the example of FIG. 6, it is shown that the node (vector) identified by the node ID “N1” corresponds to the content (object) identified by the content ID “CT1”. Further, from the node identified by the node ID "N1", it is shown that the edge identified by the edge ID "E11" is connected to the node (vector) identified by the node ID "N25". That is, in the example of FIG. 6, it is shown that the node (vector) identified by the node ID “N1” can be traced to the node (vector) identified by the node ID “N25”.

The graph information storage unit 123 is not limited to the above, and various information may be stored depending on the purpose. For example, the graph information storage unit 123 may store the length of the edge connecting each node (vector). That is, the graph information storage unit 123 may store information indicating the distance between each node (vector).

(Model information storage unit 124)
The model information storage unit 124 according to the embodiment stores information about the model. For example, the model information storage unit 124 stores model information (model data) generated by the generation process. FIG. 7 is a diagram showing an example of a model information storage unit according to the embodiment. The model information storage unit 124 shown in FIG. 7 includes items such as "model ID", "use", and "model data". Although only the models M1 and M2 are shown in FIG. 7, a large amount of model information such as M21 and M22 may be stored according to each application (prediction target).

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

For example, in the example shown in FIG. 7, the model (model M1) identified by the model ID "M1" has a purpose of "feature extraction (app)" and is used for extracting features from input content information. Show that. Further, it is shown that the model data of the model M1 is the model data MDT1.

The model M1 (model data MDT1) includes an input layer into which content information of the content is input, an output layer, and a first element which is any layer from the input layer to the output layer and belongs to a layer other than the output layer. , A second element whose value is calculated based on the first element and the weight of the first element, and each element belonging to each layer other than the output layer with respect to the content information input to the input layer is first. As an element, it is a model for making a computer function so as to output the same information as 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.

Further, it is assumed that the models M1, M2 and the like are realized by a neural network having one or a plurality of intermediate layers such as a 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. Further, the second element corresponds to a node in the next stage, which is a node to which a value is transmitted from a node corresponding to the first element. Further, the weight of the first element corresponds to a connection coefficient which is a weight considered for the value transmitted from the node corresponding to the first element to the node corresponding to the second element.

Here, it is assumed that the models M21, M22 and the like are realized by the regression model represented by "y = a1 * x1 + a2 * x2 + ... + ai * xi". In this case, for example, the first element included in the models M21 and M22 corresponds to input data (xi) such as x1 and x2. Further, 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 corresponds to any node of the input layer, and the second element can be regarded as the node of the output layer.

The model information storage unit 124 is not limited to the above, and may store various model information depending on the purpose.

(Control unit 130)
Returning to the description of FIG. 3, the control unit 130 is a controller, and is stored in a storage device inside the information processing device 100 by, for example, a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). It is realized by executing various programs (corresponding to an example of an information processing program) using the RAM as a work area. Further, the control unit 130 is a controller, and is realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 130 has an input layer, an output layer, and an input layer to an output layer into which the content information of the content is input by information processing according to the models M1, M2, etc. stored in the model information storage unit 124. It includes a first element which is one of the layers and belongs to a layer other than the output layer, and a second element whose value is calculated based on the weights of the first element and the first element, and is input to the input layer. Information similar to the information input to the input layer by performing an operation based on the weights of the first element and the first element, with each element belonging to each layer other than the output layer as the first element. Is output from the output layer.

As shown in FIG. 3, the control unit 130 includes an acquisition unit 131, a generation unit 132, a determination unit 133, a search unit 134, and a provision unit 135, and functions and operations of information processing described below. To realize or execute. The internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 3, and may be another configuration as long as it is a configuration for performing information processing described later.

(Acquisition unit 131)
The acquisition unit 131 acquires various types of information. For example, the acquisition unit 131 acquires various information from the storage unit 120. For example, the acquisition unit 131 acquires various information from the content information storage unit 121, the index information storage unit 122, the graph information storage unit 123, the model information storage unit 124, and the like. In addition, the acquisition unit 131 acquires various types of information from an external information processing device. The acquisition unit 131 acquires various information from the terminal device 10 and the information providing device 50.

The acquisition unit 131 acquires graph information in which a plurality of nodes corresponding to each of the plurality of contents are connected according to the similarity of the plurality of contents, and content information relating to one content. The acquisition unit 131 acquires graph information in which a plurality of vectors corresponding to each of the plurality of contents are concatenated according to the similarity of the plurality of contents, and content information relating to one content. The acquisition unit 131 acquires graph information in which a plurality of vectors indicating the characteristics of each of the plurality of contents are concatenated according to the similarity.

The acquisition unit 131 acquires graph information in which a plurality of vectors whose elements are feature quantities extracted from each of a plurality of contents using a predetermined model are concatenated according to the similarity. The acquisition unit 131 inputs information about a plurality of contents into a predetermined model, and a plurality of vectors having the feature amounts of the plurality of contents to be extracted as elements are concatenated according to the similarity. To get.

The acquisition unit 131 acquires trend information regarding the trend of similar contents. The acquisition unit 131 acquires trend information indicating an index value regarding the trend of similar contents. The acquisition unit 131 acquires content information related to one content from the terminal device used by the user.

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

For example, the acquisition unit 131 acquires graph information (graph data) from an external information processing device. For example, the acquisition unit 131 acquires graph information from the graph information storage unit 123. For example, the acquisition unit 131 acquires graph information. In the example of FIG. 1, the acquisition unit 131 acquires the graph information GR11.

For example, the acquisition unit 131 acquires index information (index data) from an external information processing device. For example, the acquisition unit 131 acquires index information from the index information storage unit 122. For example, the acquisition unit 131 acquires the index information of the tree structure type. In the example of FIG. 1, the acquisition unit 131 acquires the index information IND11.

In addition, the acquisition unit 131 acquires content information related to one content from the terminal device 10 used by the user. For example, the acquisition unit 131 acquires the content information of one content as a search query. For example, the acquisition unit 131 acquires a search query related to the content. The acquisition unit 131 acquires the content information CDT11 related to the content X from the terminal device 10 used by the user U1.

For example, the acquisition unit 131 acquires content information related to one content. For example, the acquisition unit 131 acquires the content information CDT 11 related to the content X from the terminal device 10. For example, the acquisition unit 131 acquires the graph information GR11 regarding the content from the graph information storage unit 123. Further, for example, the acquisition unit 131 acquires the index information IND11 used for determining the node (starting point vector) that is the starting point of the search in the graph information GR 11 from the index information storage unit 122 (see FIG. 5).

In the example of FIG. 1, the acquisition unit 131 acquires an index value of similar content from the content information storage unit 121 (see FIG. 4). The acquisition unit 131 acquires information such as the index value VL451 of the node N451 (content # 451) and the index value VL35 of the node N35 (content # 35) as shown in the similar information SLT. Further, the acquisition unit 131 acquires the threshold value TH11 from the storage unit 120 that stores the threshold value TH11.

(Generator 132)
The generation unit 132 generates various information. For example, the generation unit 132 uses the learning data (content information) stored in the content information storage unit 121 to generate a model as shown in the model information storage unit 124. For example, the generation unit 132 generates a model (autoencoder) that outputs information similar to the input content information based on the learning data acquired by the acquisition unit 131. For example, the generation unit 132 generates a model (autoencoder) that outputs the same information as the input content information, using the input content information itself as correct answer information.

For example, the generation unit 132 generates the model M1 and the like, and stores the generated model M1 and the like in the model information storage unit 124. The generation unit 132 may generate the model M1 using any learning algorithm. For example, the generation unit 132 generates the model M1 by 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 containing one or more neurons, an intermediate layer containing one or more neurons, and one or more neurons. It has an output layer including.

The generation unit 132 includes an input layer into which content information of the content is input, an output layer, a first element which is any layer from the input layer to the output layer and belongs to a layer other than the output layer, and a first element. And the second element whose value is calculated based on the weight of the first element and the second element, and each element belonging to each layer other than the output layer is set as the first element with respect to the content information input to the input layer. By performing an operation based on the weights of one element and the first element, a model that outputs the same information as the information input to the input layer from the output layer is generated.

For example, the generation unit 132 generates a model based on the training data. For example, the generation unit 132 generates a model by learning using the content information CDT1, CDT2, etc. in the content information storage unit 121 as learning data (teacher data).

For example, the generation unit 132 performs a learning process so that when the content information CDT1 is input, the model M1 outputs the same information as the content information CDT1. For example, the generation unit 132 performs a learning process so that when the content information CDT2 is input, the model M1 outputs the same information as the content information CDT2.

The generation unit 132 is not limited to the model M1 as an autoencoder, and may generate a model M21 corresponding to the content (entrance), a model M22 corresponding to the content (entry), and the like by using various learning algorithms. .. For example, the generation unit 132 generates models M21, M22, and the like using learning algorithms such as a neural network, a support vector machine (SVM), clustering, and reinforcement learning. As an example, when the generation unit 132 generates models M21, M22, etc. using a neural network, the models M21, M22, etc. include an input layer containing one or more neurons and an intermediate layer containing one or more neurons. It has an output layer containing the above neurons. The information processing device 100 does not have to have the generation unit 132 when acquiring a model from another external device such as the information providing device 50.

In the example of FIG. 1, the generation unit 132 generates an N-dimensional vector from the content information of each content by using a model for extracting the features of the content information. The generation unit 132 generates a vector from the content information of each content by using the model M1 shown in the model information storage unit 124 (FIG. 7).

In addition, the generation unit 132 generates a vector used for searching graph information from the content information corresponding to one content. In the example of FIG. 1, the generation unit 132 generates the vector corresponding to the content X by the processing as shown in the processing group PS11. The generation unit 132 inputs the content information CDT11 regarding the content X into the model M1. Specifically, the generation unit 132 inputs the content information CDT 11 including information such as the genre (category) and the content of the content X into the model M1. Then, the generation unit 132 generates a vector by using the information in the model M1 after the input of the content information CDT11. For example, the generation unit 132 generates vector data using each element in the model M1 in which the content information CDT11 is input.

In the example of FIG. 1, the generation unit 132 generates the vector VD11 by using the value of each element in the model M1 in which the content information CDT11 is input. For example, the generation unit 132 uses the value VE1 (see FIG. 10) corresponding to the neuron NL1 of the model M1 and the value VE2 (see FIG. 10) corresponding to the neuron NL2 when the content information CDT11 of the content X is input. To generate a vector. For example, the generation unit 132 may extract the value VE1 corresponding to the calculated neuron NL1 as the first-dimensional element of the vector VD11 when the content information CDT11 of the content X is input. Further, for example, the generation unit 132 generates the vector VD11 by using the value VE2 corresponding to the calculated neuron NL2 as the second-dimensional element of the vector VD11 when the content information of the content is input. In the example of FIG. 1, the generation unit 132 generates the vector VD11 such that the element of the first dimension is “35” and the element of the second dimension is “63”.

(Decision unit 133)
The determination unit 133 determines various information. The determination unit 133 determines the origin node based on the index information used for determining the origin node. For example, the determination unit 133 determines the starting point vector based on the content information acquired by the acquisition unit 131 and the information regarding the starting point vector that is the starting point for searching the graph information. For example, the determination unit 133 determines the starting point vector based on the content information and the information that serves as a reference for determining the starting point vector for searching the graph information. Further, the determination unit 133 determines the starting point vector based on the index information used for determining the starting point vector. Further, the determination unit 133 determines the starting point vector based on the index information of the tree structure type.

In the example of FIG. 1, the determination unit 133 uses the index information IND11 to determine (specify) the origin vector corresponding to one content (query). For example, the determination unit 133 uses the index information IND11 to determine (specify) the starting point vector corresponding to the vector VD11 of the content X. The determination unit 133 determines the starting point vector in the graph information GR 11 by using the vector VD11 and the index information IND11.

For example, the determination unit 133 determines the starting point vector in the graph information GR 11 by using the tree-structured index information as shown in the index information IND11 in FIG. In the example of FIG. 1, after generating the vector VD11, the determination unit 133 efficiently searches by tracing the index information IND11 from top to bottom to identify a starting point vector that is a candidate near the index information IND11. Determine the origin vector corresponding to the query (one content).

For example, the determination unit 133 determines the starting point vector corresponding to the vector VD11 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. 1, the determination unit 133 determines the node N451 as the starting point vector by tracing the index information IND11 from the root RT to the node N451.

The determination unit 133 determines an index value (determination index value) used for determination. In the example of FIG. 1, the determination unit 133 is used for determination based on information such as the index value VL451 of the node N451 (content # 451) and the index value VL35 of the node N35 (content # 35) as shown in the similar information SLT. Determine the index value.

In the example of FIG. 1, the determination unit 133 determines the average of the index values of the similar contents included in the similar information SLT as the index value for determination. The determination unit 133 determines the average of the index values of similar contents as the index value for determination. The determination unit 133 determines the average of the index value VL451 of the content # 45 and the index value VL35 of the content # 35 included in the similar information SLT as the index value for determination. In this case, the determination unit 133 calculates the total value of the index values of similar contents by adding the index value VL451 of the content # 45 and the index value VL35 of the content # 35. Then, the determination unit 133 calculates the determination index value by dividing the total value of the index values of the similar contents by the number of similar contents. For example, when the determination unit 133 has two similar contents, the content # 45 and the content # 35, the determination unit 133 divides the total value obtained by adding the index value VL451 and the index value VL35 by the number of similar contents "2". To calculate the index value for judgment. In the example of FIG. 1, the determination unit 133 determines the determination index value DVL11 as shown in the determination information VLT.

The determination unit 133 may determine any index value as the determination index value based on the index value of the similar content included in the similar information SLT. For example, the determination unit 133 may determine the highest index value among the similar information SLTs as the determination index value. For example, the determination unit 133 may determine the lowest index value among the similar information SLTs as the determination index value. For example, the determination unit 133 may determine the index value of the content most similar to the content X among the similar information SLTs as the index value for determination.

(Search unit 134)
The search unit 134 searches for various types of information. For example, the search unit 134 searches for various types of information using the information acquired by the acquisition unit 131. For example, the search unit 134 extracts various information. For example, the search unit 134 extracts various information using the information acquired by the acquisition unit 131. For example, the search unit 134 performs various determination processes. For example, the search unit 134 determines various types of information using the information acquired by the acquisition unit 131. For example, the search unit 134 searches for various information stored in the storage unit 120. For example, the acquisition unit 131 searches for various information stored in the content information storage unit 121, the index information storage unit 122, the graph information storage unit 123, the model information storage unit 124, and the like. For example, the search unit 134 performs a calculation process using various information. For example, the search unit 134 calculates the distance between the vectors using the information about the vectors. For example, the search unit 134 may use information on the length (distance) of the edge connecting each node (vector) stored in the graph information storage unit 123, or each node (from the vector information of each node). Information on the length (distance) of the edges connecting the vectors) may be calculated, and the calculated length (distance) information may be used.

The search unit 134 searches for graph information from a plurality of nodes of the graph information acquired by the acquisition unit 131, starting from the starting node that is the starting point for searching the graph information determined based on a predetermined criterion. Is to extract similar content that is similar to one content from a plurality of contents. The search unit 134 extracts similar contents starting from the starting point node determined by the determining unit 133.

The search unit 134 determines whether or not one content is popular based on the trend information acquired by the acquisition unit 131. When the index value of the similar content satisfies a predetermined criterion, the search unit 134 determines that one content is a trendy content that may be popular. The search unit 134 compares the index value of similar content with a predetermined threshold value, and if the index value of similar content is equal to or greater than the predetermined threshold value, determines that one content is trendy content.

The search unit 134 searches for graph information from a plurality of vectors of graph information acquired by the acquisition unit 131, starting from a starting point vector that is a starting point for searching graph information determined based on a predetermined criterion. , Extract similar content that is similar to one content from a plurality of contents. The search unit 134 extracts various information. For example, the search unit 134 searches the graph information with the starting point vector determined by the determining unit 133 as the starting point, and extracts similar content that is similar to one content among the plurality of contents.

The search unit 134 searches for graph information from a plurality of nodes of the graph information acquired by the acquisition unit 131, starting from the starting node that is the starting point for searching the graph information determined based on a predetermined criterion. To determine whether one content is included in a plurality of contents. The search unit 134 determines whether or not one content is included in the plurality of contents, starting from the starting point node determined by the determination unit 133.

In the example of FIG. 1, the search unit 134 searches for content similar to the content X. For example, the search unit 134 extracts similar contents of the content X by searching the graph information GR11. The search unit 134 extracts nodes located in the vicinity of the node N451 as similar contents. For example, the search unit 134 extracts nodes that are close to the node N451 as similar contents. For example, the search unit 134 extracts the nodes reachable from the node N451 as similar contents by tracing the edge starting from the node N451. For example, the search unit 134 extracts a predetermined number of nodes (for example, 2 or 10 nodes) as similar contents. For example, the search unit 134 extracts similar content of the content X by a search process as shown in FIG. In the example of FIG. 1, the search unit 134 extracts the node N451 and the node N35 as similar contents by searching the graph information GR11 starting from the node N451.

In the example of FIG. 1, the search unit 134 performs a determination process using the determination index value. The search unit 134 performs a determination process using the determination index value determined by the determination unit 133. For example, the search unit 134 determines whether or not the content X is popular by comparing the determination index value with a predetermined threshold value. For example, the search unit 134 determines that the content X is popular when the determination index value is equal to or higher than a predetermined threshold value. For example, the search unit 134 determines that the content X is not popular when the determination index value is less than a predetermined threshold value.

In the example of FIG. 1, the search unit 134 compares the determination index value DVL11 with the predetermined threshold value TH11, and determines that the determination index value DVL11 is equal to or higher than the predetermined threshold value TH11. In this case, since the search unit 134 satisfies the criterion that the determination index value DVL11 is equal to or higher than the predetermined threshold value TH11, the search unit 134 determines that the content X is in fashion as shown in the fashion determination result DRS. For example, when the search unit 134 determines that the determination index value DVL11 is less than the predetermined threshold value TH11, the search unit 134 determines that the content X is not popular.

(Providing section 135)
The providing unit 135 provides various types of information. For example, the providing unit 135 transmits various information to the terminal device 10 and the information providing device 50. For example, the providing unit 135 distributes various information to the terminal device 10 and the information providing device 50. For example, the providing unit 135 provides various information to the terminal device 10 and the information providing device 50. The providing unit 135 provides a predetermined service based on the similar contents extracted by the search unit 134. In addition, the providing unit 135 provides an information providing service regarding similar contents. The providing unit 135 provides the terminal device 10 with information regarding similar contents.

The providing unit 135 provides a predetermined service based on the similar contents extracted by the search unit 134. The providing unit 135 provides a predetermined service to the terminal device 10. The providing unit 135 provides an information providing service regarding similar contents.

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

For example, the providing unit 135 provides a service based on the determination result. In the example of FIG. 1, the providing unit 135 provides the terminal device 10 with information (trend determination information) indicating that the content X is content that may be trendy (trend content). For example, the providing unit 135 transmits trend determination information indicating that the content X is trendy content to the terminal device 10. For example, the providing unit 135 transmits trend determination information indicating that the content X is unlikely to be trendy to the terminal device 10.

[4. Information processing flow]
Next, the procedure of information processing by the information processing system 1 according to the embodiment will be described with reference to FIG. FIG. 8 is a flowchart showing an example of information processing according to the embodiment.

As shown in FIG. 8, the information processing apparatus 100 acquires content information related to one content (step S101). In the example of FIG. 1, the information processing device 100 acquires the content information CDT11 related to the content X from the terminal device 10.

The information processing device 100 acquires graph information related to a plurality of contents (step S102). For example, the information processing apparatus 100 acquires the graph information GR11 regarding the content from the graph information storage unit 123.

Then, the information processing apparatus 100 generates a vector from the content information related to one content by using the model (step S103). In the example of FIG. 1, the information processing apparatus 100 generates the vector VD11 from the content information CDT11 by using the model M1 stored in the model information storage unit 124.

Then, the information processing apparatus 100 determines the starting point vector using the generated vector and the index information (step S104). In the example of FIG. 1, the information processing apparatus 100 determines the starting point vector at the node N451 by using the vector VD11 and the index information IND11 stored in the index information storage unit 122.

Then, the information processing apparatus 100 extracts similar content similar to one content by searching the graph information (step S105). For example, the information processing apparatus 100 extracts content similar to one content as similar content by searching graph information.

Then, the information processing device 100 provides a service based on similar contents (step S106). In the example of FIG. 1, the information processing apparatus 100 provides an information providing service showing a determination result of whether or not the content X is popular based on the information of the content # 451 and the content # 35 extracted as similar content.

[5. Generation process flow]
Next, the procedure of the generation process by the information processing system 1 according to the embodiment will be described with reference to FIG. FIG. 9 is a flowchart showing an example of the generation process according to the embodiment.

As shown in FIG. 9, the information processing apparatus 100 acquires the learning data (step S201). For example, the information processing device 100 acquires learning data from the content information storage unit 121. For example, the information processing device 100 acquires the content information CDT1, CDT2, etc. as learning data from the content information storage unit 121.

After that, the information processing apparatus 100 generates a model based on the training data (step S202). For example, the information processing device 100 generates the model M1 from the content information storage unit 121 using the learning data. For example, the information processing apparatus 100 generates the model M1 so as to output the same information (content information) as the information (content information) input to the input layer from the output layer. For example, the information processing apparatus 100 generates a model M1 as an autoencoder that inputs content information of contents.

[6. Search example]
Here, an example of the search using the graph information described above is shown. The search using 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. 11 as an example. FIG. 11 is a flowchart showing an example of a search process using graph data (graph information). The search process described below is performed, for example, by the search unit 134 of the information processing apparatus 100. In addition, the objects referred to below may be read as vectors and nodes. In the following description, it is assumed that the information processing device 100 performs the search process, but the search process may be performed by another device. For example, the information processing apparatus 100 uses vector data generated from the content information of one content as a search query. For example, the information processing apparatus 100 searches graph data starting from a starting point vector determined based on vector data generated from the content information of one content and index information. In the example of FIG. 1, the information processing apparatus 100 searches the graph information GR11 starting from the node N451 which is the starting point vector determined based on the vector VD11 of the content X and the index information IND11.

Here, the neighborhood object set N (G, y) is a set of neighborhood objects associated with the edge assigned to the node y. “G” may be predetermined graph data (for example, graph information GR11 or the like). For example, the information processing apparatus 100 executes the k-nearest neighbor search process.

For example, the information processing apparatus 100 sets the radius r of the hypersphere to ∞ (infinity) (step S300) and extracts a subset S from the existing object set (step S301). For example, the information processing apparatus 100 may extract an object (node) selected as a root node (starting point vector) as a subset S. In the example of FIG. 1, the information processing apparatus 100 may extract the node N451 or the like, which is a starting point vector, as a subset S. Further, for example, a hypersphere is a virtual sphere indicating a search range. The objects included in the object set S extracted in step S301 are also included in the initial set of the object set R of the search result at the same time.

Next, the information processing apparatus 100 extracts the object having the shortest distance from the object y from the objects included in the object set S, where y is the search query object, and sets it as the object s (step S302). In the example of FIG. 1, the information processing apparatus 100 extracts the object having the shortest distance from the vector VD11, which is the search query object, among the objects included in the object set S, and uses them as the objects s. For example, the information processing apparatus 100 extracts the node N451 having the shortest distance from the vector VD11 among the objects included in the object set S and uses it as the object s. For example, in the information processing apparatus 100, when only the object (node) selected as the root node (starting point vector) is an element of the object set S, the root node (starting point vector) is extracted as the object s as a result. .. Next, the information processing apparatus 100 excludes the objects 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 (exceeds) r (1 + ε) (step S304). Here, ε is an extension element, and r (1 + ε) is a value indicating the radius of the search range (searching only the nodes within this range. The accuracy can be improved 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). 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 neighboring 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 above, 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 r (1 + ε) or less (step S307). When the distance d (u, y) between the object u and the object y is r (1 + ε) or less (step S307: Yes), the information processing apparatus 100 adds the object u to the object set S (step S308). If the distance d (u, y) between the object u and the object y is not less than or equal to r (1 + ε) (step S307: No), the information processing apparatus 100 determines (processes) step S309.

Next, the information processing apparatus 100 determines whether or not the distance d (u, y) between the object u and the object y is r or less (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 determines (processes) step S315. When the distance d (u, y) between the object u and the object y is not r or less (step S309: No), the information processing apparatus 100 determines (processes) step S315.

When the distance d (u, y) between the object u and the object y is r or less (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 an arbitrarily determined natural number. 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 does not exceed ks (step S311: No), the information processing apparatus 100 determines (processes) step S313.

When the number of objects included in the object set R exceeds ks (step S311: Yes), the information processing apparatus 100 selects the object having the longest (far) distance 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 does not match ks (step S313: No), the information processing apparatus 100 determines (processes) step S315. When the number of objects included in the object set R matches ks (step S313: Yes), the information processing apparatus 100 has the longest distance (far) from the object y among the objects included in the object set R. The distance between the object and the object y is set to a new r (step S314).

Then, the information processing apparatus 100 selects all the objects from the objects that are the elements of the object set N (G, s) in the vicinity of the objects s, and determines whether or not the objects have been stored in the object set C (step S315). .. When all the objects are selected from the objects which are the elements of the object set N (G, s) in the vicinity of the object s and are not completely stored in the object set C (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 the elements of the object set N (G, s) in the vicinity of the objects s and stored in the object set C (step S315: Yes), the information processing apparatus 100 sets the object set S. Determines whether or not is an empty set (step S316). If 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. When 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 device 100 may provide an object (node) included in the object set R to the terminal device 10 or the like that has performed the search as a search result corresponding to the search query (input object y). In the example of FIG. 1, the information processing device 100 provides the node N451 and the node N35 included in the object set R to the searched terminal device 10 and the like as search results corresponding to the search query (vector VD11 of the content X). You may. For example, the information processing device 100 provides the content # 451 corresponding to the node N451 and the content # 35 corresponding to the node N35 to the terminal device 10 used by the user U1 as content similar to the content X.

[7. effect〕
As described above, the information processing apparatus 100 according to the embodiment includes an acquisition unit 131 and a search unit 134. The acquisition unit 131 acquires graph information in which a plurality of nodes corresponding to each of the plurality of contents are connected according to the similarity of the plurality of contents, and content information relating to one content. The search unit 134 searches for graph information from a plurality of nodes of the graph information acquired by the acquisition unit 131, starting from the starting node that is the starting point for searching the graph information determined based on a predetermined criterion. Is to extract similar content that is similar to one content from a plurality of contents.

As described above, the information processing apparatus 100 according to the embodiment appropriately extracts similar contents that are similar to one content among the plurality of contents by searching the graph information with the starting point vector as the starting point. Can be done.

Further, the information processing apparatus 100 according to the embodiment has a determination unit 133. The determination unit 133 determines the origin node based on the index information used for determining the origin node. The search unit 134 extracts similar contents starting from the starting point node determined by the determining unit 133.

As described above, the information processing apparatus 100 according to the embodiment can appropriately extract similar contents by determining the starting point vector based on the index information used for determining the starting point vector.

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

As described above, the information processing apparatus 100 according to the embodiment can appropriately extract similar contents by determining the starting point vector based on the index information of the tree structure type.

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 the characteristics of each of the plurality of contents are connected according to the similarity.

As described above, the information processing apparatus 100 according to the embodiment appropriately extracts similar contents by acquiring graph information in which a plurality of vectors indicating the characteristics of each of the plurality of contents are concatenated according to the similarity. can do.

Further, in the information processing apparatus 100 according to the embodiment, in the acquisition unit 131, a plurality of vectors having feature quantities extracted from each of the plurality of contents using a predetermined model are connected according to the similarity. Get graph information.

As described above, in the information processing apparatus 100 according to the embodiment, graph information in which a plurality of vectors whose elements are feature quantities extracted from each of a plurality of contents using a predetermined model are concatenated according to the similarity. By acquiring, similar contents can be appropriately extracted.

Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 inputs information about a plurality of contents into a predetermined model, and a plurality of vectors having each feature amount of the plurality of contents to be extracted as an element. Gets the graph information concatenated according to the similarity.

As described above, in the information processing apparatus 100 according to the embodiment, by inputting information about a plurality of contents into a predetermined model, a plurality of vectors having each feature amount of the plurality of contents to be extracted are similar. By acquiring graph information linked according to gender, similar contents can be appropriately extracted.

Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires trend information regarding the trend of similar contents. The search unit 134 determines whether or not one content is popular based on the trend information acquired by the acquisition unit 131.

As described above, the information processing apparatus 100 according to the embodiment appropriately determines whether or not one content is popular by determining whether or not one content is popular based on the trend information regarding the fashion of similar contents. Can be determined.

Further, in the information processing apparatus 100 according to the embodiment, the acquisition unit 131 acquires trend information indicating an index value relating to the trend of similar contents. When the index value of the similar content satisfies a predetermined criterion, the search unit 134 determines that one content is a trendy content that may be popular.

As described above, the information processing apparatus 100 according to the embodiment determines that one content is a trendy content that may be popular when the index value of the similar content satisfies a predetermined criterion. Can be properly determined whether or not is prevalent.

Further, in the information processing apparatus 100 according to the embodiment, the search unit 134 compares the index value of similar content with a predetermined threshold value, and when the index value of similar content is equal to or higher than the predetermined threshold value, one content is popularized. Determined to be content.

As described above, the information processing apparatus 100 according to the embodiment compares the index value of the similar content with the predetermined threshold value, and when the index value of the similar content is equal to or more than the predetermined threshold value, one content is a trendy content. By determining that, it is possible to appropriately determine whether or not one content is popular.

Further, the information processing device 100 according to the embodiment has a providing unit 135. The providing unit 135 provides a predetermined service based on the similar contents extracted by the search unit 134.

As described above, the information processing apparatus 100 according to the embodiment can appropriately provide a service using information on similar contents by providing a predetermined service based on the extracted similar contents.

Further, in the information processing device 100 according to the embodiment, the acquisition unit 131 acquires content information related to one content from the terminal device used by the user. The providing unit 135 provides a predetermined service to the terminal device 10.

In this way, the information processing device 100 according to the embodiment acquires content information related to one content from the terminal device 10 used by the user, and provides the terminal device 10 with a predetermined service to provide information on similar content. It is possible to appropriately provide services using.

Further, in the information processing apparatus 100 according to the embodiment, the providing unit 135 provides an information providing service regarding similar contents.

As described above, the information processing apparatus 100 according to the embodiment can appropriately provide a service using information on similar contents by providing an information providing service on similar contents.

[8. Hardware configuration]
The information processing apparatus 100 according to the above-described embodiment is realized by, for example, a computer 1000 having a configuration as shown in FIG. FIG. 13 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device. 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). ) Has 1700.

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

The HDD 1400 stores a program executed by the CPU 1100, data used by such a program, and the like. The communication interface 1500 receives data from another device via the network N and sends it to the CPU 1100, and transmits the data generated by the CPU 1100 to the other device via the network N.

The CPU 1100 controls an output device such as a display or a printer, and an input device such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. Further, 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 the program or data to the CPU 1100 via the RAM 1200. The CPU 1100 loads the 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. And so on.

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, but as another example, these programs from another device via the network N. May be obtained.

Although some of the embodiments of the present application have been described in detail with reference to the drawings, these are examples, and various modifications are made based on the knowledge of those skilled in the art, including the embodiments described in the disclosure line of the invention. It is possible to practice the present invention in other improved forms.

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

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of the device is functionally or physically distributed / physically in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

In addition, the processes described in the above-described embodiments can be appropriately combined as long as the processing contents do not contradict each other.

Further, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the acquisition unit can be read as an acquisition means or an acquisition circuit.

1 Information processing system 100 Information processing device 121 Content information storage unit 122 Index information storage unit 123 Graph information storage unit 124 Model information storage unit 130 Control unit 131 Acquisition unit 132 Generation unit 133 Decision unit 134 Search unit 135 Providing unit 10 Terminal device 50 Information provider N network

Claims (15)

A graph information in which a plurality of nodes corresponding to each of a plurality of contents are connected according to the similarity of the plurality of contents, an acquisition unit for acquiring the content information related to one content, and an acquisition unit.
By searching the graph information from the plurality of nodes of the graph information acquired by the acquisition unit, starting from the starting node that is the starting point for searching the graph information determined based on a predetermined criterion. , A search unit that extracts similar content that is similar to the one content among the plurality of contents.
An information processing device characterized by being equipped with. A determination unit that determines the origin node based on the index information used to determine the origin node.
With more
The search unit
The information processing apparatus according to claim 1, wherein the similar contents are extracted from the starting point node determined by the determining unit. The decision unit
The information processing apparatus according to claim 2, wherein the starting node is determined based on the index information of the tree structure type. The acquisition unit
The invention according to any one of claims 1 to 3, wherein the graph information in which the plurality of nodes are connected is acquired according to the similarity of the plurality of vectors corresponding to each of the plurality of contents. Information processing device. The acquisition unit
The information processing apparatus according to claim 4, wherein the plurality of vectors showing the characteristics of each of the plurality of contents acquire the graph information in which the plurality of vectors are connected according to the similarity. The acquisition unit
4. Claim 4 characterized in that the plurality of vectors having feature quantities extracted from each of the plurality of contents using a predetermined model are concatenated according to the similarity to obtain the graph information. Alternatively, the information processing apparatus according to claim 5. The acquisition unit
By inputting information on the plurality of contents into a predetermined model, the graph information in which the plurality of vectors having the feature amounts of the plurality of contents extracted as elements are concatenated according to the similarity is obtained. The information processing apparatus according to any one of claims 4 to 6, wherein the information processing apparatus is to be acquired. The acquisition unit
Obtain trend information regarding the trend of similar content,
The search unit
The information processing apparatus according to claim 7, wherein it is determined whether or not the one content is popular based on the trend information acquired by the acquisition unit. The acquisition unit
Acquire the trend information indicating the index value regarding the trend of the similar content,
The search unit
The information processing apparatus according to claim 8, wherein when the index value of the similar content satisfies a predetermined criterion, it is determined that the one content is a trendy content that may be popular. The search unit
The index value of the similar content is compared with a predetermined threshold value, and when the index value of the similar content is equal to or greater than the predetermined threshold value, the one content is determined to be the trendy content. The information processing apparatus according to claim 9. A provider that provides a predetermined service based on the similar content extracted by the search unit,
The information processing apparatus according to any one of claims 1 to 10, further comprising. The acquisition unit
The content information related to the one content is acquired from the terminal device used by the user, and the content information is acquired.
The providing part
The information processing device according to claim 11, wherein the predetermined service is provided to the terminal device. The providing part
The information processing device according to claim 11 or 12, wherein an information providing service relating to the similar content is provided. It is an information processing method executed by a computer.
A graph information in which a plurality of nodes corresponding to each of a plurality of contents are connected according to the similarity of the plurality of contents, and an acquisition process of acquiring the content information related to one content.
By searching the graph information from the plurality of nodes of the graph information acquired in the acquisition step, starting from the starting node that is the starting point for searching the graph information determined based on a predetermined criterion. , A search process for extracting similar content that is similar to the one content among the plurality of contents.
An information processing method characterized by including. A graph information in which a plurality of nodes corresponding to each of a plurality of contents are connected according to the similarity of the plurality of contents, an acquisition procedure for acquiring the content information related to one content, and an acquisition procedure.
By searching the graph information from the plurality of nodes of the graph information acquired by the acquisition procedure, starting from the starting node that is the starting point for searching the graph information determined based on a predetermined criterion. , A search procedure for extracting similar content that is similar to the one content among the plurality of contents,
An information processing program characterized by having a computer execute.

Images