CN107169012B

CN107169012B - POI recommendation method, device, equipment and computer readable storage medium

Info

Publication number: CN107169012B
Application number: CN201710209459.6A
Authority: CN
Inventors: 俞文昌; 张伟
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2017-03-31
Filing date: 2017-03-31
Publication date: 2021-03-19
Anticipated expiration: 2037-03-31
Also published as: CN107169012A

Abstract

The invention provides a POI recommendation method, a POI recommendation device, POI recommendation equipment and a computer readable storage medium. According to the method and the device, the target POI cluster to which the target POI belongs is obtained according to the obtained target POI which is interested by the user, the homogeneous POI cluster which has a homogeneous incidence relation with the target POI cluster is further obtained according to the target POI cluster, so that the homogeneous POI cluster with the nearest structure distance can be selected according to the structure distance between the homogeneous POI cluster and the target POI and recommended to the user.

Description

POI recommendation method, device, equipment and computer readable storage medium

[ technical field ] A method for producing a semiconductor device

The present invention relates to recommendation technologies, and in particular, to a POI recommendation method, apparatus, device, and computer-readable storage medium.

[ background of the invention ]

With the development of communication technology, terminals integrate more and more functions, so that more and more corresponding Applications (APPs) are included in a system function list of the terminal. Some applications may involve Point of Interest (POI) recommendation services, where the POI information is an information element in geographic information and is information of buildings such as shops, public service sites, and bus stations based on the geographic information or service sites capable of providing services.

How to accurately recommend POI which the user may be interested in or POI which needs to be known to the user so as to improve the success rate of POI recommendation is a technical problem which needs to be solved urgently.

[ summary of the invention ]

Aspects of the present invention provide a method, an apparatus, a device and a computer-readable storage medium for POI recommendation, so as to improve the success rate of POI recommendation.

In one aspect of the present invention, a POI recommendation method is provided, including:

acquiring a target POI which is interested by a user;

obtaining a target POI cluster to which the target POI belongs according to the target POI;

obtaining a homogeneous POI cluster having a homogeneous incidence relation with the target POI cluster according to the target POI cluster;

and selecting the homogeneous POI cluster with the closest structural distance according to the structural distance between the homogeneous POI cluster and the target POI, and recommending the homogeneous POI cluster to the user.

The above-described aspect and any possible implementation manner further provide an implementation manner, where the obtaining of a target POI interested by a user includes:

obtaining the target POI according to the attribute data of the user; or

Obtaining the target POI according to the latest query operation of the user; or

Obtaining the target POI according to the current query operation of the user; or

And obtaining the target POI according to the current position of the user.

The above-described aspect and any possible implementation manner further provide an implementation manner, before obtaining, according to the target POI, a target POI cluster to which the target POI belongs, further including:

acquiring user behavior data of users in the whole network;

acquiring an association relation between every two POIs according to the user behavior data;

and carrying out POI clustering processing by adopting a community discovery algorithm according to the association relationship between every two POIs and the association parameters of the association relationship between every two POIs so as to obtain at least one POI cluster with a tree structure relationship, so as to obtain a target POI cluster to which the target POI belongs according to the target POI, and obtain a homogeneous POI cluster with a homogeneous association relationship with the target POI cluster according to the target POI cluster.

The above-mentioned aspect and any possible implementation manner further provide an implementation manner, where the association parameter of the association relationship between two POIs includes:

the support degree of the incidence relation between every two POIs; or

And the support degree of the association between every two POIs and the cosine similarity of the association between every two POIs.

The foregoing aspect and any possible implementation manner further provide an implementation manner, where performing POI clustering processing according to the association relationship between each two POIs and the association parameters of the association relationship between each two POIs by using a community discovery algorithm to obtain at least one POI cluster having a tree structure relationship includes:

filtering the association relationship between every two POIs according to the association parameters of the association relationship between every two POIs;

and carrying out POI clustering processing by adopting a community discovery algorithm according to the incidence relation between every two POIs after the filtering processing so as to obtain at least one POI cluster with a tree structure relation.

The above aspect and any possible implementation manner further provide an implementation manner, where the selecting, according to a structural distance between the homogeneous POI cluster and the target POI, a homogeneous POI cluster having a closest structural distance to recommend to the user includes:

preferentially selecting the homogeneous POI cluster of the same leaf node as the target POI cluster according to the structural distance between the homogeneous POI cluster and the target POI, and recommending the homogeneous POI cluster to the user;

and if the selected homogeneous POI cluster does not meet the number of the recommendable POIs, selecting the homogeneous POI cluster of the brother nodes of the same father node as the target POI cluster, recommending the homogeneous POI cluster to the user, and so on until the selected homogeneous POI cluster meets the number of the recommendable POIs.

In another aspect of the present invention, there is provided a POI recommending apparatus including:

the system comprises an acquisition unit, a display unit and a control unit, wherein the acquisition unit is used for acquiring a target POI which is interested by a user;

the association unit is used for acquiring a target POI cluster to which the target POI belongs according to the target POI;

the association unit is further configured to obtain a homogeneous POI cluster having a homogeneous association relationship with the target POI cluster according to the target POI cluster;

and the recommending unit is used for selecting the homogeneous POI cluster with the closest structural distance according to the structural distance between the homogeneous POI cluster and the target POI and recommending the homogeneous POI cluster to the user.

The above-mentioned aspect and any possible implementation manner further provide an implementation manner, and the obtaining unit is specifically configured to

Obtaining the target POI according to the attribute data of the user; or

And obtaining the target POI according to the current position of the user.

The above-mentioned aspect and any possible implementation manner further provide an implementation manner, and the association unit is further configured to

Acquiring user behavior data of users in the whole network;

acquiring an association relation between every two POIs according to the user behavior data; and

the support degree of the incidence relation between every two POIs; or

The above-mentioned aspects and any possible implementation further provide an implementation, and the association unit is specifically configured to

The above-described aspects and any possible implementation further provide an implementation, recommendation unit, specifically for use in

In another aspect of the present invention, there is provided an apparatus comprising:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement a POI recommendation method as provided in an aspect above.

In another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the POI recommendation method provided in the above aspect.

According to the technical scheme, the target POI cluster to which the target POI belongs is obtained according to the obtained target POI which is interested by the user, the homogeneous POI cluster with the homogeneous incidence relation with the target POI cluster is further obtained according to the target POI cluster, the homogeneous POI cluster with the closest structural distance can be selected according to the structural distance between the homogeneous POI cluster and the target POI and recommended to the user, and each divided POI cluster can meet the specific requirements of the user due to the mining of the homogeneous incidence relation, so that the method and the device are suitable for serving as a basic processing unit for recommendation, POIs which are likely to be interested by the user or POIs which need to be known by the user can be accurately recommended to the user, and the success rate of POI recommendation is improved.

In addition, by adopting the technical scheme provided by the invention, the cluster is used for replacing a single POI, and the individual information of the POI is described by using the whole information of the cluster, so that the information of the single POI is enriched, and the reliability of POI recommendation can be effectively improved.

In addition, by adopting the technical scheme provided by the invention, the POI is divided more accurately, and the situation that the division is not clear or even wrong due to the key words or information loss of the POI is avoided.

In addition, by adopting the technical scheme provided by the invention, POI with different levels and different semantic granularities can be selected according to different scene requirements.

In addition, by adopting the technical scheme provided by the invention, the user experience can be greatly improved.

[ description of the drawings ]

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed in the embodiments or the prior art descriptions will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without inventive labor.

Fig. 1 is a schematic flowchart of a POI recommendation method according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of a POI recommendation apparatus according to another embodiment of the present invention;

FIG. 3 is a block diagram of an exemplary computer system/server 12 suitable for use in implementing embodiments of the present invention.

[ detailed description ] embodiments

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.

It should be noted that the terminal according to the embodiment of the present invention may include, but is not limited to, a mobile phone, a Personal Digital Assistant (PDA), a wireless handheld device, a Tablet Computer (Tablet Computer), a Personal Computer (PC), an MP3 player, an MP4 player, a wearable device (e.g., smart glasses, smart watch, smart bracelet, etc.), and the like.

In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

Fig. 1 is a schematic flowchart of a POI recommendation method according to an embodiment of the present invention, as shown in fig. 1.

101. And acquiring a target POI which is interested by the user.

102. And obtaining a target POI cluster to which the target POI belongs according to the target POI.

103. And obtaining a homogeneous POI cluster having a homogeneous association relation with the target POI cluster according to the target POI cluster.

104. And selecting the homogeneous POI cluster with the closest structural distance according to the structural distance between the homogeneous POI cluster and the target POI, and recommending the homogeneous POI cluster to the user.

It should be noted that part or all of the execution subjects of 101 to 104 may be an application located at the local terminal, or may also be a functional unit such as a plug-in or Software Development Kit (SDK) set in the application located at the local terminal, or may also be a processing engine located in a server on the network side, or may also be a distributed system located on the network side, which is not particularly limited in this embodiment.

It is to be understood that the application may be a native app (native app) installed on the terminal, or may also be a web page program (webApp) of a browser on the terminal, and this embodiment is not particularly limited thereto.

In this way, the target POI cluster to which the target POI belongs is obtained according to the obtained target POI which is interested by the user, and then the homogeneous POI cluster having a homogeneous association relationship with the target POI cluster is obtained according to the target POI cluster, so that the homogeneous POI cluster with the closest structural distance can be selected according to the structural distance between the homogeneous POI cluster and the target POI and recommended to the user.

Optionally, in a possible implementation manner of this embodiment, in 101, a target POI in which the user is interested may be obtained specifically by adopting a plurality of methods.

In a specific implementation process, the target POI may be obtained according to the attribute data of the user. For example, if the attribute data of the user is age 20, sex is female, resident address is home, and love is shopping at a park, then the target POI can be obtained as a garden.

In another specific implementation process, the target POI may be obtained according to a latest query operation of the user. For example, if the user inquires of the Yuanming Garden within three days, then the target POI can be obtained as the Yuanming garden.

In another specific implementation process, the target POI may be obtained according to the current query operation of the user. For example, if the user is currently querying the Yuanming Garden, then the target POI may be obtained as the Yuanming garden.

In another specific implementation process, the target POI may be obtained according to a current location of the user. For example, if the current location of the user is in the garden, then the target POI may be obtained as the garden.

Optionally, in a possible implementation manner of this embodiment, before 102, constructing a POI cluster set having a tree structure may be further included.

In a specific implementation process, user behavior data of the whole network user can be specifically acquired, and further, an association relationship between every two POIs can be acquired according to the user behavior data. Then, according to the association relationship between the two POIs and the association parameters of the association relationship between the two POIs, a community discovery algorithm is adopted to perform POI clustering processing to obtain at least one POI cluster with a tree structure relationship, so as to obtain a target POI cluster to which the target POI belongs according to the target POI, and obtain a homogeneous POI cluster with a homogeneous association relationship with the target POI cluster according to the target POI cluster. Wherein each POI cluster of the at least one POI cluster may include at least one POI therein.

Therefore, the association relationship between the POI and the POI is mined out through the long-time behaviors of a large number of users, and all the POI in the whole number are connected together through the association relationship to form a POI network. Then, a community discovery algorithm is adopted to discover a cluster of POI with good cohesion in the network, namely a POI cluster, wherein the POI cluster has the characteristic of meeting the specific requirements of a large number of users in similar scenes. The scheme is a hierarchical division, and POI classification with larger granularity can be obtained by dividing POI after the first classification by using the same method again.

In the implementation process, the adopted association parameter of the association relationship between every two POIs may be a support degree of the association relationship between every two POIs, or may also be a support degree of the association relationship between every two POIs and a cosine similarity of the association relationship between every two POIs, which is not particularly limited in this embodiment.

Specifically, first, user behavior data of each user in the network-wide users within a certain time range, for example, click behavior data, retrieval behavior data, positioning trajectory data, or the like, may be collected to obtain an association relationship between each two POIs, and meanwhile, according to the collected user behavior data, association parameters of the association relationship between each two POIs, for example, a support degree of the association relationship between each two POIs and a cosine similarity of the association relationship between each two POIs, may also be further obtained.

The support degree of the association relationship between two POIs depends on the number of times that the two POIs are clicked, retrieved or located by the user continuously or simultaneously in a certain time range.

For example, if a user clicks, retrieves or locates two POIs consecutively or simultaneously within a certain time range, the support of the association between the two POIs may be increased by 1.

The cosine similarity of the association between two POIs depends on the support degree of the association between two POIs and the heat degree of each POI in the two POIs.

For example, the cosine similarity of the association between two POIs may be

If a user clicks, retrieves or locates the POI within a certain time range, the heat of the POI can be increased by 1.

After obtaining the association relationship between each two POIs and the association parameters of the association relationship between each two POIs, the association relationship between each two POIs may be filtered according to the association parameters of the association relationship between each two POIs, that is, the association relationship between each two POIs with a weak association relationship is deleted.

In the following, how to filter the association between two POIs will be described by taking the support degree of the association between two POIs as an example of the association parameter of the association between two POIs.

Two thresholds S1 and S2 may be preset for the support degree of the association relationship between two POIs, and S2 is greater than S1. Directly deleting the association relation between every two POIs with the support degree smaller than S1; directly reserving the association relationship between every two POIs with the support degree greater than or equal to S2; for the association relationship between two POIs with the support degree greater than or equal to S1 and less than S2, further judgment needs to be made to determine which POIs can be reserved and which POIs need to be deleted. For example, a threshold L is further set, and for the association relationship between two POIs with the support degree greater than or equal to S1 and less than S2, if the support degree of the association relationship between the two POIs and other POIs is less than L, the association relationship between the two POIs needs to be preserved; if the support degree of the association between the two POIs and other POIs is greater than or equal to L, the association between the two POIs needs to be deleted.

After filtering out the weaker association relationship in the association relationships between every two POIs, a network which is formed by organizing the POIs through the association relationships and takes the POIs as nodes and the association relationships as edges is obtained. Then, a community discovery algorithm can be adopted to find a cluster of POIs with a relatively close association relationship, namely a POI cluster, in the obtained network. In the community discovery algorithm, an upper limit of the data amount of each cluster of POIs, for example, 25, or the like, may be set. And taking the result of the first division as a basic unit of the next processing, namely, regarding the result as a new POI, and repeating the operation to obtain a POI division result with coarser granularity. The partitioning is terminated until no valid association between any two POI clusters is found.

In this way, a POI cluster set having a tree structure is constructed, and POI clusters in the POI cluster set are all homogeneous POI clusters having a homogeneous association relationship.

Optionally, in a possible implementation manner of this embodiment, in 104, a homogeneous POI cluster of a same leaf node as the target POI cluster may be preferentially selected according to a structural distance between the homogeneous POI cluster and the target POI, and recommended to the user. And if the selected homogeneous POI cluster does not meet the number of the recommendable POIs, selecting the homogeneous POI cluster of the brother nodes of the same father node as the target POI cluster, recommending the homogeneous POI cluster to the user, and so on until the selected homogeneous POI cluster meets the number of the recommendable POIs.

In this implementation manner, a structural distance between the target POI cluster and each homogeneous POI cluster in other POI clusters in the POI cluster set having the tree structure may be obtained according to the constructed POI cluster set having the tree structure. The smaller the structural distance between the target POI cluster and a homogeneous POI cluster is, the higher the similarity between the target POI cluster and the homogeneous POI cluster is, the more preferred the recommendation should be made to the user.

Specifically, when the selected homogeneous POI cluster is recommended to the user, the POIs in the selected homogeneous POI cluster may be recommended to the user in an order from large to small according to the heat degree of each POI in the homogeneous POI cluster.

In this embodiment, a target POI cluster to which the target POI belongs is obtained according to the obtained target POI which the user is interested in, and then a homogeneous POI cluster having a homogeneous association relationship with the target POI cluster is obtained according to the target POI cluster, so that a homogeneous POI cluster having a closest structural distance can be selected according to a structural distance between the homogeneous POI cluster and the target POI and recommended to the user.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.

In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

Fig. 2 is a schematic structural diagram of a POI recommendation apparatus according to another embodiment of the present invention, as shown in fig. 2. The POI recommending apparatus of the present embodiment may include an acquiring unit 21, an associating unit 22, and a recommending unit 23. The obtaining unit 21 is configured to obtain a target POI interested by a user; the association unit 22 is configured to obtain, according to the target POI, a target POI cluster to which the target POI belongs; the association unit 22 is further configured to obtain, according to the target POI cluster, a homogeneous POI cluster having a homogeneous association relationship with the target POI cluster; and the recommending unit 23 is configured to select, according to the structural distance between the homogeneous POI cluster and the target POI, the homogeneous POI cluster having the closest structural distance, and recommend the homogeneous POI cluster to the user.

It should be noted that, part or all of the POI recommendation apparatus provided in this embodiment may be an application of a terminal device located on a local terminal, that is, a designated vehicle, or may also be a functional unit such as a plug-in or Software Development Kit (SDK) set in the application located on the local terminal, or may also be a processing engine located in a server on a network side, or may also be a distributed system located on the network side, which is not particularly limited in this embodiment.

Optionally, in a possible implementation manner of this embodiment, the obtaining unit 21 may be specifically configured to obtain the target POI according to the attribute data of the user; or obtaining the target POI according to the latest query operation of the user; or obtaining the target POI according to the current query operation of the user; or obtaining the target POI according to the current position of the user.

Optionally, in a possible implementation manner of this embodiment, the associating unit 22 may be further configured to obtain user behavior data of users over the whole network; acquiring an association relation between every two POIs according to the user behavior data; and carrying out POI clustering processing by adopting a community discovery algorithm according to the association relationship between every two POIs and the association parameters of the association relationship between every two POIs so as to obtain at least one POI cluster with a tree structure relationship, so as to obtain a target POI cluster to which the target POI belongs according to the target POI, and obtain a homogeneous POI cluster with a homogeneous association relationship with the target POI cluster according to the target POI cluster.

Optionally, in a possible implementation manner of this embodiment, the association unit 22 may be specifically configured to perform filtering processing on the association relationship between each two POIs according to an association parameter of the association relationship between each two POIs; and carrying out POI clustering processing by adopting a community discovery algorithm according to the incidence relation between every two POIs after the filtering processing so as to obtain at least one POI cluster with a tree structure relation.

Optionally, in a possible implementation manner of this embodiment, the recommending unit 23 may specifically select, according to a structural distance between the homogeneous POI cluster and the target POI, a homogeneous POI cluster of a same leaf node as the target POI cluster preferentially, and recommend the homogeneous POI cluster to the user. And if the selected homogeneous POI cluster does not meet the number of the recommendable POIs, selecting the homogeneous POI cluster of the brother nodes of the same father node as the target POI cluster, recommending the homogeneous POI cluster to the user, and so on until the selected homogeneous POI cluster meets the number of the recommendable POIs.

Specifically, when the recommending unit 23 recommends the selected homogeneous POI cluster to the user, the POIs in the selected homogeneous POI cluster may be recommended to the user in descending order according to the heat of each POI in the homogeneous POI cluster.

In this way, by recommending a homogeneous POI cluster to the user, which is homogeneous with the target POI of interest to the user, by using the POI semantic structure, the structure can better capture the internal connection between POIs, so that the POI which is homogeneous with the POI of interest to the user can be better found.

It should be noted that the method in the embodiment corresponding to fig. 1 may be implemented by the POI recommendation apparatus provided in this embodiment. For a detailed description, reference may be made to relevant contents in the embodiment corresponding to fig. 1, and details are not described here.

In this embodiment, the association unit obtains, according to the target POI which is interested by the user and is obtained by the obtaining unit, a target POI cluster to which the target POI belongs, and further obtains, according to the target POI cluster, a homogeneous POI cluster having a homogeneous association relationship with the target POI cluster, so that the recommendation unit can select, according to a structural distance between the homogeneous POI cluster and the target POI, the homogeneous POI cluster having a closest structural distance, and recommend to the user.

FIG. 3 illustrates a block diagram of an exemplary computer system/server 12 suitable for use in implementing embodiments of the present invention. The computer system/server 12 shown in FIG. 3 is only one example and should not be taken to limit the scope of use or functionality of embodiments of the present invention.

As shown in FIG. 3, computer system/server 12 is in the form of a general purpose computing device. The components of computer system/server 12 may include, but are not limited to: one or more processors or processing units 16, a storage device or system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.

Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server 12 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. The computer system/server 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.

The computer system/server 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with the computer system/server 12, and/or with any devices (e.g., network card, modem, etc.) that enable the computer system/server 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 44. Also, the computer system/server 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet) via the network adapter 20. As shown, network adapter 20 communicates with the other modules of computer system/server 12 via bus 18. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the computer system/server 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.

The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, implementing the POI recommendation method provided in the embodiment corresponding to fig. 1.

Another embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the POI recommendation method provided in the embodiment corresponding to fig. 1.

In particular, any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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 context of this document, 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, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like 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).

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A POI recommendation method is characterized by comprising the following steps:

acquiring a target POI which is interested by a user;

obtaining a homogeneous POI cluster having a homogeneous incidence relation with the target POI cluster according to the target POI cluster; wherein each POI cluster in the at least one POI cluster with the tree structure is a homogeneous POI cluster with a homogeneous incidence relation; the construction mode of the at least one POI cluster with the tree structure comprises the following steps: all the POIs in the total amount are connected together to form a POI network through the association relationship between the POIs; discovering at least one POI cluster with good cohesion in the network by adopting a community discovery algorithm;

2. The method of claim 1, wherein the obtaining a target POI of interest to the user comprises:

obtaining the target POI according to the attribute data of the user; or

And obtaining the target POI according to the current position of the user.

3. The method according to claim 1, wherein before obtaining, according to the target POI, a target POI cluster to which the target POI belongs, the method further comprises:

acquiring user behavior data of users in the whole network;

4. The method according to claim 3, wherein the association parameters of the association relationship between two POIs comprise:

the support degree of the incidence relation between every two POIs; or

5. The method according to claim 3, wherein the performing POI clustering processing according to the association relationship between the two POIs and the association parameters of the association relationship between the two POIs by using a community discovery algorithm to obtain at least one POI cluster having a tree structure relationship comprises:

6. The method according to any one of claims 1 to 5, wherein the selecting, according to the structural distance between the homogeneous POI cluster and the target POI, the homogeneous POI cluster with the closest structural distance to recommend the user comprises:

7. A POI recommendation apparatus, comprising:

the association unit is further configured to obtain a homogeneous POI cluster having a homogeneous association relationship with the target POI cluster according to the target POI cluster; wherein each POI cluster in the at least one POI cluster with the tree structure is a homogeneous POI cluster with a homogeneous incidence relation; the construction mode of the at least one POI cluster with the tree structure comprises the following steps: all the POIs in the total amount are connected together to form a POI network through the association relationship between the POIs; discovering at least one POI cluster with good cohesion in the network by adopting a community discovery algorithm;

8. Device according to claim 7, characterized in that the acquisition unit is specifically configured to

Obtaining the target POI according to the attribute data of the user; or

And obtaining the target POI according to the current position of the user.

9. The apparatus of claim 7, wherein the association unit is further configured to associate the received data with the specific device

Acquiring user behavior data of users in the whole network;

10. The apparatus according to claim 9, wherein the association parameters of the association relationship between two POIs include:

the support degree of the incidence relation between every two POIs; or

11. Device according to claim 9, characterized in that the association unit is specifically configured to

12. Device according to any of claims 7 to 11, characterized by a recommendation unit, in particular for recommending

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

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-6.

14. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method of any one of claims 1 to 6.