CN111625724A

CN111625724A - Information processing method, information processing device, electronic equipment and storage medium

Info

Publication number: CN111625724A
Application number: CN202010470117.1A
Authority: CN
Inventors: 王麓淞
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Baidu Online Network Technology Beijing Co Ltd; Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2020-05-28
Filing date: 2020-05-28
Publication date: 2020-09-04

Abstract

The application discloses an information processing method, an information processing device, an information processing system and a storage medium, and relates to the field of information processing. The specific implementation scheme is as follows: determining a current scene type based on the current location; determining at least one candidate scene type related to the current scene type, and determining a first candidate POI set based on the at least one candidate scene type; and taking at least part of POI in the first candidate POI set as a recommended POI.

Description

Information processing method, information processing device, electronic equipment and storage medium

Technical Field

The present application relates to the field of information processing. The present application relates to an information processing method, an information processing apparatus, an electronic device, and a storage medium.

Background

In the related art, a map display information Point (POI) is a uniform map area display content, in which all POI points within a certain geographic range are recalled according to an attribute of the POI itself, and are prioritized. However, the above scheme cannot accurately capture the change of the user's image reading requirement, and therefore cannot ensure that the POI recommended for the user better meets the current requirement of the user.

Disclosure of Invention

The disclosure provides an information processing method, an information processing device, an electronic device and a storage medium.

According to an aspect of the present disclosure, there is provided an information processing method applied to a client, including:

determining a current scene type based on the current location;

determining at least one candidate scene type related to the current scene type, and determining a first candidate POI set based on the at least one candidate scene type;

and taking at least part of POI in the first candidate POI set as a recommended POI.

According to an aspect of the present disclosure, there is provided an information processing apparatus including:

the scene matching module is used for determining the type of the current scene based on the current position;

a selecting module, configured to determine at least one candidate scene type related to the current scene type, and determine a first candidate POI set based on the at least one candidate scene type;

and the recommendation display module is used for taking at least part of POI in the first candidate POI set as a recommendation POI.

According to another aspect of the present disclosure, there is provided an electronic device including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the above-described method.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the above method.

According to the technology of the application, the related candidate scene type is deduced according to the current scene corresponding to the current position, and then the final recommended POI is determined according to the candidate scene type, so that the recommendation of the scene level is achieved. Therefore, different recommended contents in different scenes can be realized, the recommended minimum particles are improved from the POI level to the scene level, and the POI recommendation is carried out according to the current scene, so that the instant requirement of a user can be met.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:

FIG. 1 is a schematic flow chart diagram of an information processing method according to an embodiment of the application;

FIG. 2 is a schematic diagram of a scenario according to an embodiment of the present application;

FIG. 3 is a schematic diagram of an association relationship between scenes according to an embodiment of the present application;

FIG. 4 is a schematic diagram of the relationship between scene types and the relationship between a scene and a POI according to an embodiment of the present application;

FIG. 5 is a schematic diagram illustrating a POI recommendation based on a scene where a user is currently located according to an embodiment of the present application;

FIG. 6 is a first diagram illustrating a first component structure of an information processing apparatus according to an embodiment of the present application;

FIG. 7 is a diagram illustrating a second exemplary composition of an information processing apparatus according to an embodiment of the present disclosure;

fig. 8 is a block diagram of an electronic device for implementing the information processing method according to the embodiment of the present application.

Detailed Description

The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

An embodiment of the present invention provides an information processing method, applied to a client, as shown in fig. 1, including:

s101: determining a current scene type based on the current location;

s102: determining at least one candidate scene type related to the current scene type, and determining a first candidate POI set based on the at least one candidate scene type;

s103: and taking at least part of POI in the first candidate POI set as a recommended POI.

For a particular user, the reading requirements presented by the user when the user is in different contextual states, such as commuting, going-to-trip, planning-before-trip, etc., can vary greatly. However, the scene where the user is located is relatively abstract, and is difficult to judge under the condition of lacking user behaviors and standards, so that the scheme provided by the embodiment forms a 'scene' type as a substitute of a real scene by mainly taking the geographic position as a basis. According to the scheme provided by the embodiment, POI recommendation can be performed according to scene types, so that the client can acquire different recommended POIs in different scenes.

In S101, the trigger manner for obtaining the current location may be to start a map application installed on the client.

The current position may be obtained based on a GPS, and accordingly, the client may obtain the current position from the GPS.

In the embodiment of the present application, a certain position may have a corresponding feature, i.e., a scene type.

It should be further noted that although a feature corresponding to a location is a certain scene type, a scene type does not correspond to only a location, that is, a scene type may correspond to one or more area ranges and/or one or more locations.

For example, a scene type a may correspond to the

region ranges

1 and 2, or a scene type may correspond to the position (x1, y1), the position (x2, y 2).

In a preferred example, one or more area ranges correspond to the same scene type, and features at one or more positions within one area range are all of the same scene type.

Assuming that a certain position is a railway station, setting a scene type corresponding to the position as a railway station or a traffic junction area scene; for another example, if a location has a well-known historical building, the scene type of the location may be a sight. The geographic location with a certain scene type (characteristic) is not unique, for example, a train station scene type may correspond to a plurality of geographic locations, and it is assumed that

train stations

1, 2, and 3 may exist in a certain city, that is, the

train stations

1, 2, and 3 are all traffic junction scenes; or, in a scene of a scenic spot, there may be multiple geographic locations in a city, such as a palace and a famous residence, and these geographic locations correspond to the same scene attribute and are all scenic spots.

Furthermore, the division mode of the area range corresponding to the scene type, taking a railway station as an example, can set the geographical positions within a range of N meters around the railway station as the center as the traffic junction scene; here, N is a positive integer, and the range of N meters may be set according to practical situations, such as 200 meters or 300 meters, or more or less, and is not exhaustive here.

In S102, the determining at least one candidate scene type related to the current scene includes:

determining at least one candidate scene type related to the current scene type based on the incidence relation among the scene types; wherein the incidence relation between the scene types comprises: and each preset scene type in the at least one preset scene type is associated with at least one related scene type.

Specifically, the embodiment may acquire the association relationship between the scene types before executing the foregoing steps. The following is a description of the manner of determining the association relationship between scene types:

classifying and counting the historical behaviors of the users in a certain area to obtain a position point set which is in a certain area range and is interested by the users; the relationship between the scene types is derived based on statistics for the user's historical behavior. As shown in fig. 2, the statistical data of the locations (or more retrieved locations) of a location a within a certain scenic spot area range, which are interesting to users, are identified by boxes 1 to 4 in the figure as location areas retrieved by more users within the area range of the location a. For example, a user at any position a in the area may have strong demands for nearby subway stations, other scenic spots, business circles, and distant urban railway stations.

Furthermore, after data statistics is carried out by combining a plurality of key areas, it is found that users in areas of similar or same types have strong requirements for specific other types of scenes, and the user requirements among similar scenes have similarity. Based on this, an association model between scene types is established, assuming that there are a plurality of relationships between scenes as shown in fig. 3: a user in scene type a may be interested in scene type B and scene type D; users in the B scene type may be interested in the AD scene type; users within the C scene type may be interested in the B scene type.

When the correspondence relationship between the scenes is determined, the aforementioned S102 of the present embodiment may be executed.

Referring to fig. 3, when the current scene type of a certain user is the B scene type, it may be determined that the user is interested in the AD scene type according to the correspondence between the scene types, so that the scene type AD may be used as the candidate scene type in S102.

Still further, in S102, the determining a first set of candidate POIs based on the at least one candidate scene type includes:

determining at least one POI relevant to at least one candidate scene type according to the characteristics corresponding to the at least one POI; generating the first set of candidate POIs based on the at least one POI related to the at least one candidate scene type.

Based on the foregoing steps, it can be known that the location area or the location includes a certain feature, one of the features may be a scene type, that is, based on the candidate scene type, the feature may be selected from scene type features corresponding to the POIs, and at least one POI matched with the candidate scene type is selected and added to the first candidate POI set. The at least one POI may be all POIs included in the map application, and may also be a part of POIs currently displayed in all POIs.

Also referring to fig. 3, if it is determined that the candidate scene types are scene type a and scene type D in the graph according to the correspondence between scenes, POIs related to scene type a and scene type D may be recommended. It should be noted that, recommending POIs related to scene type a and scene type D is not necessarily that the recommended POI points are within the geographical range of the AD scene, but that the features of the recommended POI points are related to the AD scene type.

S102 may be further described with reference to fig. 4, assuming that the current scene type where the user is located is determined to be scene B; determining that the scenes which may be interested by the user (i.e. the aforementioned candidate scene types) may be scene types a and D according to the corresponding relationship between the scenes;

POIs that can migrate out based on scene type a (i.e., POIs associated with candidate scene types) may include POI1, POI 2, POI 3 as shown in fig. 4; POIs that can migrate out based on scene type D (i.e., POIs associated with candidate scene types) may include POI 4, POI5, POI6, POI 7 as shown in fig. 4;

that is, the first candidate POI set may include the above POI1, POI 2, POI 3, POI 4, POI5, POI6, and POI 7.

In S103, there may be several processing manners:

in one approach, if all POIs in the first candidate POI set are within the current presentation range, all POIs in the first candidate POI set may be taken as recommended POIs. Displaying the recommended POI in the map application.

In another mode, at least a part of POIs in the current display range are selected as recommended POIs from at least one POI included in the first candidate POI set.

That is to say, according to the current display range, determining a part of POIs in the first candidate POI set within the current display range as recommended POIs, and displaying the recommended POIs.

As shown in fig. 5, in an example of this embodiment, a current scene type is determined to be a according to a current position, and a candidate scene type is determined to be a scene type B, D according to a corresponding relationship between scenes; the related POIs migrated from scene type B and scene type D form a first candidate POI set { POI1, POI 2, POI 3, POI 4, POI5, POI6, POI 7 }; according to the current display range of the current map application, the recommended POIs including POI 2, POI 3, POI 4, POI5 and POI 7 can be displayed in the map, and POI1 and POI6 are not in the current display range, so that the candidate POIs are not displayed temporarily, or the POIs are not taken as the recommended POIs temporarily.

It should also be understood that if the current display range is adjusted, the recommended POI may be determined again and displayed according to the first candidate POI set and the current display range. For example, in the case of the scenario shown in fig. 5, if the display scale of the map application is adjusted, the current display range may cover more POIs, and then, by re-combining the current display range, it is possible that the POIs 1 and POI6 are displayed as recommended POIs in the current display range. Of course, the display scale may also be adjusted, so the display range may cover fewer POIs, and the POIs needed to be displayed are determined by combining the current display range again.

In another way, at least one POI included in the first candidate POI set may be prioritized, and the top M POIs with the highest priority are selected as recommended POIs according to a result of the prioritized ranking, and the recommended POIs are displayed in a map application.

In this manner, the priority may be determined based on the weight of the POI. M is an integer greater than or equal to 1, and M may be set according to an actual situation, for example, may be 10, that is, the top 10 are selected as the recommended POIs.

In addition, this method may be combined with the second method, that is, after the top M POIs with priorities are obtained based on the priority ranking, the current display range may be combined with the map application, and then a part of POIs from the top M POIs may be selected as recommended POIs, and finally the recommended POIs are displayed in the current display range.

Based on the foregoing processing, the present embodiment may increase the recommended minimum particles from the POI level to the scene level based on the division of the scene and the migration of the recommended content. The candidate scene type can be deduced according to the current scene corresponding to the position, and then the related recommended POI is displayed to the user according to the deduced candidate scene type, so that the recommendation of the scene level is achieved. Therefore, different recommended contents in different scenes can be realized, and the instant requirements of the user are met.

In yet another example, the method may further include: and acquiring the characteristic information of the user, and determining a second candidate POI set based on the characteristic information of the user. The characteristic information of the user may include attribute characteristics of the user, an interest habit of the user, and the like.

The feature information of the user may be acquired in the following manner: obtained from various applications installed by the client.

Since different users have their own user attributes and interests, in one example, two pieces of information can be extracted, one is the basic attributes of the user, such as the gender, presence or absence of a car, age, income, etc.; and secondly, the interest habits of the user are accumulated based on the long-term use of the user and comprise the diet types and the playing types which are interested by the user.

The acquisition of the user's habits of interest may be obtained based on the user's historical behavior, such as the user's historical browsing history, historical operations, historical locations (POIs), and the like.

Of course, the above is only an exemplary illustration, and there may be more ways to acquire the feature information of the user in the actual processing, and the content that the feature information of the user may include may also be more, and is not exhaustive.

In addition, the same user may have different characteristics at different times or time periods, so statistics or acquisition of the characteristic information of the user may be periodically updated, and the updating manner may be the same or different for each updating (the updating manner is not limited here). For example, the feature information of a certain acquired user includes female, low income, age 20-25, and traveling interest; another time, for example, every 1 year, the user's feature information is retrieved, and there may be some different features, for example, the user's features may include: women, middle-aged income, age 25-30, hobbies, gourmet, etc.

Determining a second candidate POI set based on the feature information of the user, including: and selecting at least one POI with the highest matching degree with the characteristic information of the user from the at least one POI to generate a second candidate POI set.

It should be understood that, in this example, the POI needs to have a feature, and the features of different POIs may be the same or different. Additionally, the characteristics of the POI can be predetermined and/or preset. For example, a POI may have multiple features, some of which may be pre-analyzed and/or some of which may be directly set. That is, the characteristics of the POI itself may include inherent characteristics of the POI itself, and also a part of the characteristics of the corresponding POI, which may be determined according to the interest of a specific user group.

The way of analyzing in advance to obtain at least one feature corresponding to the POI may be: and clustering the POI according to the user behavior, and giving a specific characteristic type to the POI.

Specifically, the method comprises the following steps: when a POI is focused by a plurality of users of the same type, the POI can be considered attractive to a specific type of user, and the feature types specific to the type of user can be migrated to the POI. Here, the features of the POI may be weighted, and the method of determining the features of the POI may be performed by establishing a model to perform cluster screening, and the like, to finally determine the features of the POI, which is not described in detail.

The determination of the feature type of the POI needs to be based on long-term user behavior statistics. In the resulting base data, each POI will have its corresponding feature or features for matching with the user. Still further, for a POI, the features clustered thereon are not unitary, but multi-dimensional and multi-angular.

For example, after a POI is clustered, obtaining at least one feature of the POI and a corresponding weight thereof may include: women (70% or 0.7 by weight), ages 20-30 (60% or 0.6 by weight), moderate income levels (50% or 0.5 by weight). Of course, it is also possible to list only at least one feature of the POI, for example, it may include: female, age 20-30, income level moderate.

In the process of matching the POI and the user, the pursuit is not all coincidence but "approximate similarity", where the approximate similarity is understood to mean that the matching degree or the similarity between the features of the user and the features of the POI is high.

Based on the foregoing description, in this example, a partial area in the map may be displayed in the display area, and this partial area is taken as the current display range in this example, at this time, it may be considered that the user generates a POI point for an area (within the current display range) by opening the map application;

and then, all the characteristics of the user are taken out, and at least one POI with the highest matching degree with the characteristic information of the user is selected from at least one POI contained in the current display range of the map application. Adding at least one POI with the highest matching degree with the feature information of the user to the second candidate POI set.

That is, according to all the features of the user, the POI points in the area are matched one by one. The matching method is an integration system, and for example, the method can be as follows: different scores may be obtained for different types of features.

Specifically, when the characteristics of the user are matched with any one of the characteristics of the POI, a score corresponding to the characteristics is given to the POI, and the score is dynamically given by the characteristic matching degree; if the more features of the user coincide with the POI, the higher the score corresponding to the POI.

And finally, taking the L POI (L can be an integer greater than or equal to 1) points with the highest score in the current display range as a final return result, namely at least one POI with the highest matching degree, and recommending.

For example, when the feature information of one POI point and the user includes the feature of "male", a score corresponding to the feature of "male" is given to the POI, such as 2.5; the POI hits the characteristics of 'high income' and 'vehicle-in' in the characteristic information of the user, the scores of the two characteristics are accumulated, such as 1 score and 1.4 score, and finally, the score of the matching degree of the POI and the characteristic information of the user is 4.9 score.

And then determining whether to recommend the POI or determine whether to add the POI as a recommended POI to a second candidate POI set for display according to the ranking position of the score of each POI in all POI sets (or a set consisting of all POIs contained in the current display range).

It should be understood that the score corresponding to each feature of the POI may be preset. In the process of scoring the characteristics matched with the POI and the user, the score of each characteristic can be directly used to obtain the final score; alternatively, the weights of the features of the POI may be multiplied by the score to obtain a final score.

In another example, the foregoing processes may be combined, that is, the first candidate POI set and the second candidate POI set may be combined to determine a recommended POI to be displayed.

In this example, the manner of determining the first candidate POI set and the second candidate POI set is the same as that in the foregoing example, and is not described again.

Unlike the foregoing example, the processing of this example may include:

merging the first candidate POI set and the second candidate POI set to obtain a candidate POI total set;

carrying out priority ranking on at least one POI contained in the candidate POI total set to obtain a priority ranking result;

and determining at least part of POI in the candidate POI total set as the recommended POI according to the priority ranking result.

Namely, the POIs contained in the first candidate POI set and the second candidate POI set can be subjected to union processing to obtain a total set containing all POIs; and then selecting the POI ranked at the top L as a recommended POI according to the priority ranking of all POIs, and finally displaying the recommended POI.

The method further comprises the steps of deleting part of POI which are not in the current display range of the map application from the first candidate POI set to obtain a new first candidate POI set; and merging the new first candidate POI set and the second candidate POI set to obtain a total candidate POI set, and then executing subsequent processing.

Still further, the prioritization of the POIs may be determined based on the weights of the foregoing examples, for example, the weights may be determined according to the matching degree of the POIs and the features of the user, and the prioritization may be determined.

Based on the example, a recommendation set more suitable for the user can be provided for different users, the personalized requirements of the user are met, the recommendation is more matched with the user, and the image reading experience of the user is improved

The scheme provided by the embodiment can be applied to electronic equipment, such as a smart phone, a tablet computer and the like.

An embodiment of the present application further provides an information processing apparatus, as shown in fig. 6, including:

a scene matching module 61, configured to determine a current scene type based on a current location;

a selecting module 62, configured to determine at least one candidate scene type related to the current scene type, and determine a first candidate POI set based on the at least one candidate scene type;

a recommendation display module 63, configured to use at least some POIs in the first candidate POI set as recommendation POIs.

A selecting module 62, configured to determine at least one candidate scene type related to the current scene type based on an association relationship between scene types;

wherein the incidence relation between the scene types comprises: and each preset scene type in the at least one preset scene type is associated with at least one related scene type.

A selecting module 62 configured to determine at least one POI related to at least one candidate scene type according to a feature corresponding to the at least one POI; generating the first set of candidate POIs based on the at least one POI related to the at least one candidate scene type.

As shown in fig. 7, the apparatus provided in this embodiment further includes:

and the feature matching module 64 is configured to acquire feature information of the user, and determine a second candidate POI set based on the feature information of the user.

And the feature matching module 64 is configured to select at least one POI with the highest matching degree with the feature information of the user from the at least one POI, and generate a second candidate POI set.

The recommendation display module 63 is configured to merge the first candidate POI set and the second candidate POI set to obtain a candidate POI total set; carrying out priority ranking on at least one POI contained in the candidate POI total set to obtain a priority ranking result; and determining at least part of POI in the candidate POI total set as the recommended POI according to the priority ranking result.

By adopting the scheme, the related candidate scene type can be deduced according to the current scene corresponding to the position of the user, and then the related recommended POI displayed to the user is determined according to the candidate scene type, so that the recommendation of the scene level is realized. Therefore, different recommended contents in different scenes can be realized, the recommended minimum particles are improved from the POI level to the scene level, and the POI recommendation is carried out according to the current scene of the user, so that the instant requirement of the user can be met.

According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.

As shown in fig. 8, it is a block diagram of an electronic device according to an information processing method of an embodiment of the present application. The electronic device may be the aforementioned deployment device or proxy device. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.

As shown in fig. 8, the electronic apparatus includes: one or more processors 801, memory 802, and interfaces for connecting the various components, including a high speed interface and a low speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 8 illustrates an example of a processor 801.

The memory 802 is a non-transitory computer readable storage medium as provided herein. The memory stores instructions executable by at least one processor, so that the at least one processor executes the information processing method provided by the application. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to execute the information processing method provided by the present application.

The memory 802 is a non-transitory computer-readable storage medium, and can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the information processing method in the embodiment of the present application (for example, the scene matching module 61, the selecting module 62, the recommendation presentation module 63, and the feature matching module 64 shown in fig. 7). The processor 801 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 802, that is, implements the information processing method in the above-described method embodiments.

The memory 802 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 802 may include high speed random access memory and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 802 optionally includes memory located remotely from the processor 801, which may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The electronic device of the information processing method may further include: an input device 803 and an output device 804. The processor 801, the memory 802, the input device 803, and the output device 804 may be connected by a bus or other means, and are exemplified by a bus in fig. 8.

The input device 803 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the electronic device, such as a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick, or other input device. The output devices 804 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

According to the technical scheme of the embodiment of the application, the related candidate scene type is deduced according to the current scene corresponding to the position of the user, and then the related recommended POI displayed to the user is determined according to the candidate scene type, so that the recommendation of the scene level is achieved. Therefore, different recommended contents in different scenes can be realized, the recommended minimum particles are improved from the POI level to the scene level, and the POI recommendation is carried out according to the current scene of the user, so that the instant requirement of the user can be met.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.

The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An information processing method is applied to a client and comprises the following steps:

determining a current scene type based on the current location;

2. The method of claim 1, wherein determining at least one candidate scene type related to the current scene comprises:

determining at least one candidate scene type related to the current scene type based on the incidence relation among the scene types;

3. The method of claim 1, wherein said determining a first set of candidate POIs based on said at least one candidate scene type comprises:

determining at least one POI relevant to at least one candidate scene type according to the characteristics corresponding to the at least one POI;

generating the first set of candidate POIs based on the at least one POI related to the at least one candidate scene type.

4. The method according to any one of claims 1-3, further comprising:

and acquiring the characteristic information of the user, and determining a second candidate POI set based on the characteristic information of the user.

5. The method of claim 4, wherein the determining a second set of candidate POIs based on the user's feature information comprises:

and selecting at least one POI with the highest matching degree with the characteristic information of the user from the at least one POI to generate a second candidate POI set.

6. The method of claim 4, further comprising:

7. An information processing apparatus comprising:

8. The apparatus of claim 7, wherein the selecting module is configured to determine at least one candidate scene type related to the current scene type based on an association relationship between scene types;

9. The apparatus according to claim 7, wherein the selecting module is configured to determine at least one POI related to at least one candidate scene type according to a feature corresponding to the at least one POI; generating the first set of candidate POIs based on the at least one POI related to the at least one candidate scene type.

10. The apparatus of any of claims 7-9, wherein the apparatus further comprises:

and the feature matching module is used for acquiring feature information of the user and determining a second candidate POI set based on the feature information of the user.

11. The apparatus of claim 10, wherein the feature matching module is configured to select at least one POI with a highest matching degree with the feature information of the user from the at least one POI to generate the second candidate POI set.

12. The apparatus of claim 10, wherein the recommendation presentation module is configured to merge the first candidate POI set and the second candidate POI set to obtain a total set of candidate POIs; carrying out priority ranking on at least one POI contained in the candidate POI total set to obtain a priority ranking result; and determining at least part of POI in the candidate POI total set as the recommended POI according to the priority ranking result.

13. An electronic device, comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.