CN114417169A

CN114417169A - Information recommendation optimization method, device, medium, and program product

Info

Publication number: CN114417169A
Application number: CN202210091707.2A
Authority: CN
Inventors: 杨海军; 徐倩; 杨强
Original assignee: WeBank Co Ltd
Current assignee: WeBank Co Ltd
Priority date: 2022-01-26
Filing date: 2022-01-26
Publication date: 2022-04-29

Abstract

The application discloses an information recommendation optimization method, equipment, a medium and a program product, wherein the information recommendation optimization method comprises the following steps: acquiring personalized recommendation information corresponding to a user to be recommended, and extracting geographical position information matched with the user intention of the user to be recommended; performing personalized information theme query according to the personalized recommendation information and performing information theme query according to a target position area determined by the geographic position information to obtain each target recommendation theme; and recommending information to the user to be recommended according to each target recommendation theme. The information recommendation method and device solve the technical problem that in the prior art, the accuracy of information recommendation is low.

Description

Information recommendation optimization method, device, medium, and program product

Technical Field

The present application relates to the field of artificial intelligence in financial technology (Fintech), and in particular, to a method, device, medium, and program product for information recommendation optimization.

Background

With the continuous development of financial science and technology, especially internet science and technology, more and more technologies (such as distributed technology, artificial intelligence and the like) are applied to the financial field, but the financial industry also puts higher requirements on the technologies, for example, higher requirements on the distribution of backlog in the financial industry are also put forward.

For the acquisition of information, users are very concerned about the dynamics of certain places related to the users, so that many map positioning related applications are generated, for example, in a map search mode of popular comment, the users can directly see published user comments when ordering restaurants or places, and in a short video application, the users can watch more short videos of the places with cards when choosing the places with cards, but the positions adopted by the applications are usually preset by merchants, and in many cases, the positions are not the geographical positions which the users want to pay attention to, so that the matching degree of the geographical position information adopted when information recommendation is currently carried out and the user intention is not high, and the accuracy of the information recommendation is influenced.

Disclosure of Invention

The present application mainly aims to provide an information recommendation optimization method, device, medium, and program product, and aims to solve the technical problem in the prior art that the accuracy of information recommendation is low.

In order to achieve the above object, the present application provides an information recommendation optimizing method, including:

acquiring personalized recommendation information corresponding to a user to be recommended, and extracting geographical position information matched with the user intention of the user to be recommended;

performing personalized information theme query according to the personalized recommendation information and performing information theme query according to a target position area determined by the geographic position information to obtain each target recommendation theme;

and recommending information to the user to be recommended according to each target recommendation theme.

Optionally, the step of performing personalized information theme query according to the personalized recommendation information and performing information theme query according to the target location area determined by the geographic location information to obtain each target recommendation theme includes:

acquiring each personalized recommendation theme corresponding to the personalized recommendation information;

determining the target position area according to the geographical position information, and acquiring position recommendation topics corresponding to the target position area;

and determining each target recommendation topic by sequencing each personalized recommendation topic and each position recommendation topic.

and sequencing the position recommendation topics according to the personalized recommendation information to determine the target recommendation topics.

Optionally, the geographical location information comprises a user location,

the step of determining the target location area from the geographical location information comprises:

determining each information gathering position area within a preset distance range of the user positioning position;

visually displaying each information gathering position area, and receiving feedback information of the user to be recommended to each information gathering position area;

and determining the target position area in each information gathering position area according to the feedback information.

Optionally, the step of determining each information aggregation location area within a preset distance range of the user positioning location includes:

acquiring information release position data corresponding to each piece of release information within a preset distance range of the user positioning position;

clustering the information release position data to obtain a position clustering result;

and determining each information gathering position area according to the position clustering result.

Optionally, the step of obtaining the position recommendation topics corresponding to the target position area includes:

acquiring each piece of release information in the target position area;

performing topic clustering on each piece of issued information to obtain a first topic clustering result;

and determining position recommendation topics corresponding to the target position area according to the first topic clustering result.

Optionally, the personalized recommendation information comprises retrieval information,

the step of performing personalized information theme query according to the personalized recommendation information and performing information theme query according to the target position area determined by the geographical position information to obtain each target recommendation theme comprises the following steps:

retrieving and recalling according to the retrieval information to obtain a personalized recall set;

retrieving and recalling according to the geographic position information to obtain a position recall set;

clustering the recall samples in the personalized recall set and the recall samples in the position recall set together to obtain a second theme clustering result;

and determining position recommendation topics corresponding to the target position area according to the second topic clustering result.

The present application further provides an information recommendation optimizing apparatus, which includes:

the system comprises an acquisition module, a recommendation module and a recommendation module, wherein the acquisition module is used for acquiring personalized recommendation information corresponding to a user to be recommended and extracting geographical position information matched with the user intention of the user to be recommended;

the determining module is used for carrying out personalized information theme query according to the personalized recommendation information and carrying out information theme query according to the target position area determined by the geographic position information to obtain each target recommendation theme;

and the recommending module is used for recommending information to the user to be recommended according to each target recommending theme.

Optionally, the determining module is further configured to:

Optionally, the geographic location information includes a user location position, and the determining module is further configured to:

Optionally, the determining module is further configured to:

acquiring each piece of release information in the target position area;

Optionally, the personalized recommendation information includes retrieval information, and the determining module is further configured to:

retrieving and recalling according to the geographic position information to obtain a positioning position recall set;

The present application further provides an electronic device, the electronic device including: the information recommendation optimizing method comprises a memory, a processor and a program of the information recommendation optimizing method stored on the memory and capable of running on the processor, wherein the program of the information recommendation optimizing method can realize the steps of the information recommendation optimizing method when being executed by the processor.

The present application also provides a computer-readable storage medium having stored thereon a program for implementing an information recommendation optimization method, which when executed by a processor, implements the steps of the information recommendation optimization method as described above.

The present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the information recommendation optimization method as described above.

Compared with the technical means of information recommendation based on the geographical position preset by a merchant in the prior art, the method comprises the steps of firstly obtaining personalized recommendation information corresponding to a user to be recommended, extracting geographical position information matched with the user intention of the user to be recommended, carrying out personalized information theme query according to the personalized recommendation information and carrying out information theme query according to a target position area determined by the geographical position information to obtain each target recommendation theme, wherein the geographical position information is matched with the user intention, and the target recommendation theme obtained by carrying out information theme query according to the target position area determined by the geographical position information is matched with the user intention so as to be based on each target recommendation theme, the information recommendation method and the information recommendation device have the advantages that the information recommendation is carried out on the user to be recommended, on the basis of carrying out personalized recommendation, the information recommendation is carried out in the target position area based on the user intention, so that the recommended information is related to the target position area and simultaneously accords with the user intention, namely is matched with the user intention, the technical defect that the accuracy of the information recommendation is influenced due to the fact that the matching degree of the geographical position information adopted when the information recommendation is carried out at present and the user intention is not high when the information recommendation is carried out based on the geographical position preset by a merchant in the prior art is overcome, and the accuracy of the information recommendation is improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.

Fig. 1 is a schematic flow chart of a first embodiment of an information recommendation optimization method according to the present application;

fig. 2 is a schematic flow chart illustrating information recommendation according to search information in the information recommendation optimization method of the present application;

FIG. 3 is a flowchart illustrating a second embodiment of an information recommendation optimizing method according to the present application;

FIG. 4 is a schematic view illustrating a process of obtaining an information topic according to a user portrait in the information recommendation optimization method of the present application;

fig. 5 is a schematic structural diagram of a hardware operating environment related to an information recommendation optimization method in the embodiment of the present application.

The objectives, features, and advantages of the present application will be further described with reference to the accompanying drawings.

Detailed Description

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Example one

An embodiment of the present application provides an information recommendation optimization method, which is applied to a first federal participant, and in the first embodiment of the information recommendation optimization method of the present application, referring to fig. 1, the information recommendation optimization method includes:

step S10, obtaining personalized recommendation information corresponding to a user to be recommended, and extracting geographic position information matched with the user intention of the user to be recommended;

in this embodiment, it should be noted that the personalized recommendation information may be a user portrait or search information input by a user, the search information may be a search keyword or an information classification tag, and the geographic location information may be location information determined based on a current location position of the user, such as current satellite location information of the user, location information based on a geographic name input by the user, or location information extracted from the personalized recommendation information. The geographical position information is determined by input information of the user to be recommended or by the positioning position of the user to be recommended, so that the geographical position information is matched with the intention of the user to be recommended.

Step S20, according to the personalized recommendation information, performing personalized information theme inquiry and according to the target position area determined by the geographical position information, performing information theme inquiry to obtain each target recommendation theme;

in this embodiment, specifically, according to the personalized recommendation information, a retrieval recall is performed in a preset personalized index library to obtain a personalized recall set, and an information topic corresponding to each recall sample in the personalized recall set is used as a first type topic; acquiring each piece of release information in a target position area corresponding to the geographic position information, and taking an information topic corresponding to each piece of release information as a second type topic; and selecting each target recommendation theme from each first type theme and each second type theme.

As an example, where the target location area is an information aggregation location area marked on a map, location information corresponding to all pieces of published information may be clustered in advance, so that a plurality of information aggregation location areas are marked on the map. The step of obtaining each piece of release information in a target location area corresponding to the geographical location information includes:

and inquiring a target position area corresponding to the geographic position information in each information clustering position area according to the corresponding relation between the position information and the information clustering position area, and retrieving and recalling in a preset positioning index library according to the target position area to obtain each release information in the target position area.

It should be noted that the preset personalized index library includes a text index library and a classification index library, where the text index library is configured to perform retrieval and recall according to a keyword of an input text content, the classification index library is configured to perform retrieval and recall according to an input recall sample type, the personalized recall set includes at least one personalized recall sample, the personalized recall sample may be a published video or a published document, the preset positioning index library is configured to perform sample recall according to a positioning index corresponding to geographic position information, and release information in a target position area corresponding to the geographic position information that can be recalled by the preset positioning index library is used as a recall sample.

As an example, the process of establishing the preset positioning index library is as follows:

acquiring geographical position information corresponding to release information of a target area, and clustering the geographical position information to obtain a plurality of position information clustering clusters; taking the corresponding position area of each position information clustering in the target area as an information clustering position area; acquiring position release information of each information clustering position area; respectively carrying out information clustering on the position release information in each information clustering position area to obtain an information clustering cluster corresponding to each information clustering position area; and respectively distributing corresponding information subjects for each information clustering cluster, and establishing the preset positioning index library according to the incidence relation between the information clustering position area and the information subjects.

As an example, the process of building the classification index library is as follows:

acquiring each piece of release information, and classifying each piece of release information according to a preset classification model to obtain a classification label corresponding to each piece of release information; and establishing a classification index library according to the release information and the classification labels corresponding to the releases. The preset classification model may be a text classification model, and when the release information is information in a non-text form, the release information may be converted into a text and then classified, for example, when the release information is voice information, the voice information is converted into a text in a voice-to-text manner.

As an example, the process of building the text index library is as follows:

acquiring the text content of each piece of release information, and clustering each text content to obtain a text cluster; and respectively distributing corresponding search keywords for each text clustering cluster, and establishing the text index database according to each release information and the corresponding search keywords.

And step S30, recommending information to the user to be recommended according to each target recommendation subject.

In this embodiment, each target recommendation topic is ranked to obtain a target recommendation topic list, and the target recommendation topic list is recommended to the target to be recommended.

As an example, the personalized recommendation information includes retrieval information, and the step of ranking each of the target recommendation topics to obtain a target recommendation topic list includes:

and calculating first text similarity between the text vector corresponding to the retrieval information and the text vector corresponding to each target recommendation topic, and sorting the target recommendation topics according to the first text similarity to obtain a target recommendation topic list, for example, sorting the target recommendation topics from large to small according to the similarity of the first text.

As an example, the personalized recommendation information includes a user portrait, and the step of ranking each of the target recommendation topics to obtain a target recommendation topic list includes:

and calculating second text similarity between the text vector corresponding to the user image and the text vector corresponding to each target recommendation topic, and sequencing each target recommendation topic according to the second text similarity to obtain a target recommendation topic list.

As an example, the step of performing personalized information topic query according to the personalized recommendation information and performing information topic query according to the target location area determined by the geographic location information to obtain each target recommendation topic includes:

step A10, retrieving and recalling according to the personalized recommendation information to obtain a personalized recall set;

in this embodiment, specifically, the retrieval information is used as an index, and a corresponding at least one recall sample is queried in a preset personalized index library to obtain a personalized recall set.

Step A20, retrieving and recalling according to the geographic position information to obtain a positioning position recall set;

in this embodiment, specifically, according to the geographic location information, a corresponding recall sample is searched in the preset location index library to obtain the location recall set.

Step A30, the recall samples in the personalized recall set and the recall samples in the positioning position recall set are sequenced to obtain each target recommendation theme.

In this embodiment, by calculating the similarity between the text vector corresponding to the topic of the recall sample in the personalized recall set and the text vector corresponding to the retrieval information, and by calculating the similarity between the text vector corresponding to the topic of the recall sample in the positioning position recall set and the text vector corresponding to the retrieval information, each recall sample is scored, and a score value corresponding to each recall sample is obtained; and sequencing the recall samples according to the size of each score value to obtain a sequencing result, and selecting a target recommendation topic from topics corresponding to the recall samples according to the sequencing result, wherein if the recall samples are texts, the topics corresponding to the recall samples can be topics of the texts, and if the recall samples are videos, the topics corresponding to the recall samples can be video names of the videos. The purpose of determining the information subject recommended by the information according to the retrieval information and the geographic position information is achieved, the information subject can be acquired according to the geographic position information matched with the intention of the user on the basis of acquiring the multidimensional information subject according to the retrieval information, the acquired information subject is more comprehensive, and the comprehensiveness of information recommendation can be improved.

As an example, as shown in fig. 2, a schematic flowchart of information recommendation according to retrieval information is shown, where the retrieval information includes personalized recommendation information and geographic location information, where the personalized recommendation information may be text content or a classification tag, where the text content may be partial text content of published information, the classification tag may be a classification tag of the published information, the geographic location information is satellite positioning, a landmark, or a search range, and the like, the satellite positioning may be coordinates of the satellite positioning, the landmark may be a geographic name of the geographic location, the search range may be a search range of the geographic location, for example, which city or which street is specific, and a recall sample corresponding to the retrieval information is recalled by the retrieval recall.

The step of performing personalized information theme query according to the personalized recommendation information and performing information theme query according to the target position area determined by the geographical position information to obtain each target recommendation theme comprises:

step B10, determining the target location area according to the geographical location information, and acquiring location recommendation topics corresponding to the target location area;

in this embodiment, specifically, according to an association relationship between location information and information aggregation location areas, a target location area corresponding to the geographic location information is determined in each of the information aggregation location areas; and extracting to-be-selected information topics of each piece of release information in the target position hotspot area, and selecting each position recommendation topic in each to-be-selected information topic.

As an example, the step of selecting each location recommendation topic from each information topic to be selected includes:

and acquiring the click rate of each piece of published information, and selecting the information topics to be selected of the published information with the click rate ranking in the top preset number as position recommendation topics.

In another practical implementation manner, the specific implementation process of determining the target location area according to the geographic location information may refer to the following details in step D10 to step D30, and will not be described herein again.

Step B20, ranking the position recommendation topics according to the personalized recommendation information, and determining the target recommendation topics.

In this embodiment, specifically, text similarity between a text vector corresponding to the personalized recommendation information and a text vector corresponding to each of the position recommendation topics is calculated to obtain third text similarities; and selecting each target recommendation theme from the position recommendation themes according to the similarity of each third text. As an example, 10 position recommendation topics with the third text similarity ranked in the top may be selected as the target recommendation topic.

The step of obtaining the position recommendation topics corresponding to the target position area comprises the following steps:

step B11, acquiring each release message in the target position area;

step B12, performing topic clustering on each piece of issued information to obtain a first topic clustering result;

and step B13, determining position recommendation topics corresponding to the target position area according to the first topic clustering result.

In this embodiment, it should be noted that the first topic clustering result may be an information topic clustering cluster.

Specifically, an information topic of each piece of release information in a preset time span in the target position area is used as an information topic to be clustered; clustering the information topics to be clustered to obtain information topic clustering clusters; and matching corresponding information topics for each information topic cluster to obtain position recommendation topics corresponding to the target position areas.

As an example, the step of clustering each information topic to be clustered to obtain each information topic cluster includes:

performing clustering analysis on the information subject to be clustered by using a hierarchical clustering algorithm to obtain an initial clustering result; and further carrying out clustering analysis on the initial clustering result by using a K-means clustering algorithm to obtain a final clustering result, namely clustering clusters for each information topic, wherein the initial clustering result can be the number of clusters, the cluster center and the like.

Wherein the personalized recommendation information includes retrieval information,

step C10, retrieval recall is carried out according to the retrieval information to obtain a personalized recall set;

step C20, retrieval recall is carried out according to the geographic position information to obtain a positioning position recall set;

step C30, clustering the recall samples in the personalized recall set and the recall samples in the position recall set together to obtain a second theme clustering result;

and step C40, determining position recommendation topics corresponding to the target position area according to the second topic clustering result.

In this embodiment, it should be noted that the second topic clustering result may be a sample information topic clustering cluster.

Specifically, according to the retrieval information, retrieving and recalling are carried out in a preset personalized index library to obtain a personalized recall set; according to the geographic position information, retrieving and recalling in a preset positioning index library to obtain a position recall set; acquiring a sample information theme of each recall sample in the personalized recall set and a sample information theme of the position recall set; clustering each sample information topic to obtain each sample information topic cluster; and matching corresponding information topics for each sample information topic cluster to obtain position recommendation topics corresponding to the target position area.

As an example, the second topic clustering result may be a location information clustering cluster.

The individual recall samples in the personalized recall set and the recall samples in the position recall set are clustered together to obtain a second theme clustering result; determining position recommendation topics corresponding to the target position area according to the second topic clustering result, wherein the determining step comprises the following steps:

acquiring positioning position information corresponding to each recall sample in the personalized recall set and positioning position information corresponding to each recall sample in the position recall set; clustering the positioning position information according to the connectivity of the positioning position information of each recalled sample in the geographical position to obtain a clustering cluster of the positioning position information; positioning position information with the distance from the cluster center of each positioning position information cluster within a preset distance range is used as target positioning position information; and taking the information subject of the recall sample corresponding to the target positioning position information as a position recommendation subject. The purpose of determining the information recommendation subject of information recommendation according to the geographical position information matched with the user intention is achieved.

Compared with the technical means of information recommendation based on the geographical position preset by a merchant in the prior art, the information recommendation optimization method comprises the steps of firstly obtaining personalized recommendation information corresponding to a user to be recommended, extracting geographical position information matched with the user intention of the user to be recommended, carrying out personalized information theme query according to the personalized recommendation information and carrying out information theme query according to a target position area determined by the geographical position information to obtain each target recommendation theme, wherein the geographical position information is matched with the user intention, and the target recommendation theme obtained by carrying out the information theme query according to the target position area determined by the geographical position information is matched with the user intention so as to carry out information recommendation on the user to be recommended according to each target recommendation theme, the information recommendation method and the information recommendation device realize information recommendation in the target position area based on the user intention on the basis of personalized recommendation, so that the recommended information is related to the target position area and also accords with the user intention, namely is matched with the user intention, and therefore the technical defect that the accuracy of information recommendation is influenced due to the fact that the matching degree of the geographical position information adopted when information recommendation is currently performed and the user intention is not high when information recommendation is performed based on the geographical position preset by a merchant in the prior art is overcome, and the accuracy of information recommendation is improved.

Example two

Further, referring to fig. 3, based on the first embodiment of the present application, in another embodiment of the present application, the same or similar contents to the first embodiment described above may be referred to the above description, and are not repeated again in the following. On this basis, the step of performing personalized information theme query according to the personalized recommendation information and performing information theme query according to the target position area determined by the geographical position information to obtain each target recommendation theme comprises the following steps:

step S21, obtaining each personalized recommendation theme corresponding to the personalized recommendation information;

step S22, determining the target location area according to the geographical location information, and acquiring location recommendation topics corresponding to the target location area;

step S23, determining each target recommendation topic by ranking each personalized recommendation topic and each position recommendation topic.

In this embodiment, specifically, each personalized recommendation topic corresponding to the personalized recommendation information is obtained; determining a target position area corresponding to the geographic position information in each information gathering position area according to the incidence relation between the position information and the information gathering position area; extracting information topics to be selected of each piece of release information in the target position hot spot area, and selecting each position recommendation topic in each information topic to be selected; calculating the similarity between the text vector corresponding to the personalized recommendation information and the text vector corresponding to each personalized recommendation subject, and calculating the similarity between the text vector corresponding to the personalized recommendation information and each position recommendation subject to obtain the similarity of each fourth text corresponding to the personalized recommendation information; sequencing each personalized recommendation theme and each position recommendation theme according to the similarity of each fourth text to obtain a theme sequencing result; and selecting each target recommendation theme from each personalized recommendation theme and each position recommendation theme according to the theme sorting result.

As an example, the personalized recommendation information may be a user portrait, and as shown in fig. 4, a schematic flow diagram of acquiring an information topic for information recommendation according to a user portrait in this embodiment of the application is shown, where the personalized recommendation engine is configured to determine a corresponding personalized recommendation topic based on a user portrait, the document index library may be a preset personalized index library, the information retrieval may be a retrieval recall, the general hotspot recommendation may be used to recommend hotspot information with a top ranking click rate in the whole network, the hotspot location may be an information aggregation location area, the current hotspot area may be a target location area, and a process of recommending a hotspot topic in the hotspot location may be a process of acquiring recommendation topics in each location of the target location area.

As an example, the specific implementation process of obtaining each location recommendation topic corresponding to the target location area may refer to the specific contents in step B11 to step B13, which are not described herein again.

Wherein the geographical location information comprises a user positioning location, and the step of determining the target location area according to the geographical location information comprises:

step D10, determining each information gathering position area within the preset distance range of the user positioning position;

step D20, visually displaying each information gathering position area, and receiving feedback information of the user to be recommended to each information gathering position area;

step D30, determining the target location area in each information gathering location area according to the feedback information.

In this embodiment, specifically, each information gathering location area whose distance from the user positioning location is within a preset distance range is determined; visually displaying each information gathering position area to a user to be recommended, and receiving feedback information of the user to be recommended to each information gathering position area, wherein the feedback information may be an operation command representing that the user selects each information gathering position area, such as a check command or a click command; and determining the target position area in each information gathering position area according to the feedback information. Therefore, the purpose of determining the target position area in a man-machine interaction mode is achieved, and the degree of association between the selected position area and the user to be recommended is improved.

Wherein the step of determining each information gathering location area within a preset distance range of the user positioning location comprises:

step D11, obtaining information release position data corresponding to each release information within the preset distance range of the user positioning position;

step D12, clustering the information release position data to obtain a position clustering result;

and D13, determining each information gathering position area according to the position clustering result.

In this embodiment, specifically, each piece of release information within a preset distance range of the user positioning position is acquired; extracting corresponding information release position data in each piece of release information, wherein the information release position data can be position coordinates corresponding to the release information or place names corresponding to the release information; clustering the information issuing position data according to the connectivity of the information issuing position data on the geographical position to obtain information clustering clusters of each position, namely position clustering results; and taking the position area covered by each position information cluster as a target position area. Therefore, the purpose of clustering according to the geographic position information to obtain the position information clustering cluster is achieved, and a foundation is laid for information recommendation according to the geographic position information.

It should be noted that, in a map search mode of popular comment, a user may directly see published user comments when ordering a restaurant or a place, or in a short video application, the user may view more information recommendation methods such as a short video of a place where the user clicks when selecting the place where the user clicks, recommended information is generally limited to a certain information type, and there is a limitation on information amount, for example, data of a video type or data of a text type, and a geographic location of the recommended information is generally limited to a certain place, for example, a certain dining room or a certain scenic spot, and there is a limitation on an information channel, so that the comprehensiveness of information recommendation may be affected.

The embodiment of the application provides an information recommendation optimization method, namely, obtaining each personalized recommendation theme corresponding to the personalized recommendation information; determining the target position area according to the geographical position information, and acquiring position recommendation topics corresponding to the target position area; and determining each target recommendation topic by sequencing each personalized recommendation topic and each position recommendation topic. The information subject to be recommended can be obtained from the multidimensional personalized recommendation information and the geographical position information respectively, the purpose of obtaining the information recommendation subject in a multidimensional mode is achieved, the information recommendation subject has the personalized recommendation subject obtained from the whole network and the position recommendation subject related to the geographical position and obtained from a specific target position area, the requirements of users on information amount and information channels can be met simultaneously, and therefore the comprehensiveness of information recommendation is improved.

EXAMPLE III

An embodiment of the present application further provides an information recommendation optimizing apparatus, where the information recommendation optimizing apparatus includes:

Optionally, the determining module is further configured to:

acquiring each piece of release information in the target position area;

By adopting the information recommendation optimization method in the embodiment, the information recommendation optimization device provided by the invention solves the technical problem of low accuracy of information recommendation. Compared with the prior art, the beneficial effects of the information recommendation optimizing device provided by the embodiment of the invention are the same as those of the information recommendation optimizing method provided by the embodiment, and other technical features in the information recommendation optimizing device are the same as those disclosed by the embodiment method, and are not repeated herein.

Example four

An embodiment of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the information recommendation optimization method in the first embodiment.

Referring now to FIG. 5, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 5, the electronic device may include a processing means (e.g., a central processing unit, a graphic processor, etc.) that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage means into a Random Access Memory (RAM). In the RAM, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device, ROM and RAM are trained on each other via the bus. An input/output (I/O) interface is also connected to the bus.

Generally, the following systems may be connected to the I/O interface: input devices including, for example, touch screens, touch pads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, and the like; output devices including, for example, Liquid Crystal Displays (LCDs), speakers, vibrators, and the like; storage devices including, for example, magnetic tape, hard disk, etc.; and a communication device. The communication means may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device with various systems, it is to be understood that not all illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means, or installed from a storage means, or installed from a ROM. The computer program, when executed by a processing device, performs the above-described functions defined in the methods of the embodiments of the present disclosure.

The electronic equipment provided by the invention adopts the information recommendation optimization method in the embodiment, and the technical problem of low accuracy of information recommendation is solved. Compared with the prior art, the electronic device provided by the embodiment of the invention has the same beneficial effects as the information recommendation optimization method provided by the embodiment, and other technical features in the electronic device are the same as those disclosed by the embodiment method, which are not repeated herein.

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

EXAMPLE five

The present embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon for performing the method for information recommendation optimization in the first embodiment.

The computer readable storage medium provided by the embodiments of the present invention may be, for example, a USB flash disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination thereof. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

The computer-readable storage medium may be embodied in an electronic device; or may be present alone without being incorporated into the electronic device.

The computer readable storage medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring personalized recommendation information corresponding to a user to be recommended, and extracting geographical position information matched with the user intention of the user to be recommended; performing personalized information theme query according to the personalized recommendation information and performing information theme query according to a target position area determined by the geographic position information to obtain each target recommendation theme; and recommending information to the user to be recommended according to each target recommendation theme.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the names of the modules do not in some cases constitute a limitation of the unit itself.

The computer-readable storage medium provided by the invention stores the computer-readable program instructions for executing the information recommendation optimization method, and solves the technical problem of low accuracy of information recommendation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiment of the invention are the same as the beneficial effects of the information recommendation optimization method provided by the embodiment, and are not repeated herein.

EXAMPLE six

The computer program product solves the technical problem of low accuracy of information recommendation. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the invention are the same as those of the information recommendation optimization method provided by the embodiment, and are not repeated herein.

The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.

Claims

1. An information recommendation optimization method is characterized by comprising the following steps:

2. The information recommendation optimization method according to claim 1, wherein the step of performing personalized information subject query according to the personalized recommendation information and performing information subject query according to the target location area determined by the geographic location information to obtain each target recommendation subject comprises:

3. The information recommendation optimization method according to claim 1, wherein the step of performing personalized information subject query according to the personalized recommendation information and performing information subject query according to the target location area determined by the geographic location information to obtain each target recommendation subject comprises:

4. The information recommendation optimization method of claim 2 or 3, wherein the geographical location information comprises a user location,

5. The information recommendation optimization method of claim 4, wherein the step of determining each information gathering location area within a preset distance range of the user location position comprises:

6. The information recommendation optimization method according to claim 2 or 3, wherein the step of obtaining the position recommendation topics corresponding to the target position area comprises:

acquiring each piece of release information in the target position area;

7. The information recommendation optimization method of claim 1, wherein the personalized recommendation information comprises retrieval information,

8. An electronic device, characterized in that the electronic device comprises:

at least one processor; and the number of the first and second groups,

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 steps of the information recommendation optimization method of any of claims 1 to 7.

9. A computer-readable storage medium, having a program for implementing an information recommendation optimization method stored thereon, the program being executed by a processor to implement the steps of the information recommendation optimization method according to any one of claims 1 to 7.

10. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the information recommendation optimization method according to any one of claims 1 to 7 when executed by a processor.