CN107169014A

CN107169014A - POI recommends method, device, equipment and computer-readable recording medium

Info

Publication number: CN107169014A
Application number: CN201710210064.8A
Authority: CN
Inventors: 刘巍
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: 2017-09-15
Anticipated expiration: 2037-03-31
Also published as: CN107169014B

Abstract

The present invention provides a kind of POI and recommends method, device, equipment and computer-readable recording medium.The embodiment of the present invention is by by visit capacity of the user to single POI, it is mapped to the visit capacity of the affiliated POI clusters of single POI, and then it is delivered to the visit capacity for associating POI clusters that there is incidence relation with the affiliated POI clusters of single POI, to find interest level of the user to association cluster, and the visit capacity of user's POI clusters affiliated to single POI can be delivered to have not visited with the affiliated POI clusters of single POI have an incidence relation associates POI clusters, result in effective user individual data, therefore, user individual data can be based on, accurately to user recommend the user may POI interested or it should be understood that POI, so as to improve the success rate of POI recommendations.

Description

POI recommends method, device, equipment and computer-readable recording medium

【Technical field】

Recommend method, device, equipment and computer-readable storage the present invention relates to recommended technology, more particularly to a kind of POI Medium.

【Background technology】

With the development of the communication technology, terminal is integrated with increasing function, so that the systemic-function row of terminal More and more corresponding applications (Application, APP) are contained in table.It can be related to some points of interest in some applications (Point of Interest, POI) recommendation service, POI is an information word in geography information, is based on geographical letter Retail shop, public service website and bus station of breath etc. build or can provided the information of the services sites of service.

However, because each user has personalization features, all recommending identical POI be able to not certainly will expire to all users How the individual demand of each user of foot, therefore, provide the user individual data of user, accurately to recommend to be somebody's turn to do to user User may POI interested or it should be understood that POI, be the skill of a urgent need to resolve to improve the success rate of POI recommendations Art problem.

【The content of the invention】

The many aspects of the present invention provide a kind of POI and recommend method, device, equipment and computer-readable recording medium, use To improve the success rate of POI recommendations.

An aspect of of the present present invention recommends method there is provided a kind of POI, including：

Obtain user target POI interested in user visit capacity；

According to visit capacity of the user to the target POI, the user is obtained to the target POI belonging to the POI The statistics visit capacity of cluster；

According to statistics visit capacity of the user to the target POI clusters, the user is obtained to association POI clusters Visit capacity is transmitted, there is incidence relation between the association POI clusters and the target POI clusters；

According to transmission visit capacity of the user to the association POI clusters, the user is obtained to the association POI groups The interest level data of cluster, using the user individual data as the user.

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the association are closed System includes at least one in following incidence relation：

Homogeneity incidence relation；

Isomery incidence relation；And

Global homogeneity incidence relation.

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the association are closed It is for homogeneity incidence relation；It is described according to statistics visit capacity of the user to the target POI clusters, obtain the user couple The transmission visit capacity of POI clusters is associated, including：

Obtain homogeneity association attenuation coefficient；

Attenuation coefficient is associated with the homogeneity to the statistics visit capacity of the target POI clusters according to the user, obtained Transmission visit capacity of the user to the association POI clusters.

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the association are closed It is for isomery incidence relation；It is described according to statistics visit capacity of the user to the target POI clusters, obtain the user couple The transmission visit capacity of POI clusters is associated, including：

Obtain isomery association attenuation coefficient；

Attenuation coefficient is associated with the isomery to the statistics visit capacity of the target POI clusters according to the user, obtained Transmission visit capacity of the user to the association POI clusters.

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the association are closed It is for global homogeneity incidence relation；It is described according to statistics visit capacity of the user to the target POI clusters, obtain described use Family to associate POI clusters transmission visit capacity, including：

Obtain statistics visit capacity of the user to other POI clusters, other described POI clusters and the target POI groups There is global homogeneity incidence relation between cluster and the association POI clusters；

According to the user to the statistics visit capacity of the target POI clusters and the user to other described POI clusters Statistics visit capacity, obtain the transmission visit capacity of the user to the association POI clusters.

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the acquisition are used Family target POI interested, including：

According to the attribute data of the user, the target POI is obtained；Or

According to the nearest inquiry operation of the user, the target POI is obtained；Or

According to the current inquiry operation of the user, the target POI is obtained；Or

The position being currently located according to the user, obtains the target POI.

Aspect as described above and any possible implementation, it is further provided a kind of implementation, it is described according to institute Visit capacity of the user to the target POI is stated, the user is obtained and the statistics of the target POI clusters belonging to the POI is accessed Before amount, in addition to：

Obtain the user behavior data of the whole network user；

According to the user behavior data, the incidence relation between POI two-by-two is obtained；

According to the relevant parameter of the incidence relation between the incidence relation between the POI two-by-two and the POI two-by-two, adopt Community discovery algorithm is used, POI clustering processings are carried out, to obtain at least one POI cluster with tree structure relation, for root According to the target POI, obtain the target POI clusters belonging to the target POI, and according to the target POI clusters, obtain with There is the POI clusters of homogeneity incidence relation between the target POI clusters.

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the POI two-by-two Between incidence relation relevant parameter, including：

The support of incidence relation between the POI two-by-two；Or

The cosine phase of incidence relation between the support of incidence relation between the POI two-by-two and the POI two-by-two Like degree.

Aspect as described above and any possible implementation, it is further provided a kind of implementation, it is described according to institute The relevant parameter of the incidence relation between the incidence relation between POI two-by-two and the POI two-by-two is stated, is calculated using community discovery Method, carries out POI clustering processings, to obtain at least one POI cluster with tree structure relation, including：

According to the relevant parameter of the incidence relation between the POI two-by-two, the incidence relation between the POI two-by-two is entered Row filtration treatment；

According to the incidence relation between the POI two-by-two after the filtration treatment, using community discovery algorithm, POI is carried out Clustering processing, to obtain at least one POI cluster with tree structure relation.

Aspect as described above and any possible implementation, it is further provided a kind of implementation, it is described according to institute The relevant parameter of the incidence relation between the incidence relation between POI two-by-two and the POI two-by-two is stated, is calculated using community discovery Method, carries out POI clustering processings, after at least one the POI cluster of acquisition with tree structure relation, in addition to：

Obtain the support of the incidence relation between the POI two-by-two not in the tree structure under same specified node Degree；

According to the incidence relation between the POI two-by-two under the specified node same not in the tree structure and institute The support of the incidence relation between the POI two-by-two not in the tree structure under same specified node is stated, POI groups are carried out Cluster isomery association process, to obtain the isomery incidence relation between POI clusters two-by-two, for according to the target POI clusters, obtaining Must have isomery incidence relation between the target POI clusters associates POI clusters.

Obtain the description data of each POI clusters at least one described POI cluster；The description data include comment number According to in branding data at least one of；

According to the description data, the Expressive Features of each POI clusters are obtained；

According to the Expressive Features of each POI clusters, the processing of POI clusters global association is carried out, to obtain POI groups two-by-two Global homogeneity incidence relation between cluster, for according to the target POI clusters, obtaining and having between the target POI clusters There are the association POI clusters of global homogeneity incidence relation.

Another aspect of the present invention there is provided a kind of POI recommendation apparatus, including：

Acquiring unit, the visit capacity for obtaining user target POI interested in the user；

Associative cell, for according to visit capacity of the user to the target POI, obtaining the user to the POI The statistics visit capacity of affiliated target POI clusters；

The associative cell, is additionally operable to the statistics visit capacity to the target POI clusters according to the user, obtains described User has between the association POI clusters and the target POI clusters and associated to the transmission visit capacity of association POI clusters System；

Construction unit, for according to transmission visit capacity of the user to the association POI clusters, obtaining the user couple The interest level data of the association POI clusters, using the user individual data as the user.

Homogeneity incidence relation；

Isomery incidence relation；And

Global homogeneity incidence relation.

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the association are closed It is for homogeneity incidence relation；The associative cell, specifically for

Obtain homogeneity association attenuation coefficient；And

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the association are closed It is for isomery incidence relation；The associative cell, specifically for

Obtain isomery association attenuation coefficient；And

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the association are closed It is for global homogeneity incidence relation；The associative cell, specifically for

Obtain statistics visit capacity of the user to other POI clusters, other described POI clusters and the target POI groups There is global homogeneity incidence relation between cluster and the association POI clusters；And

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the acquisition list Member, specifically

According to the attribute data of the user, the target POI is obtained；Or

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the association table Member, is additionally operable to

Obtain the user behavior data of the whole network user；

According to the user behavior data, the incidence relation between POI two-by-two is obtained；And

The support of incidence relation between the POI two-by-two；Or

Aspect as described above and any possible implementation, it is further provided a kind of implementation, the association table Member, specifically for

Obtain the support of the incidence relation between the POI two-by-two not in the tree structure under same specified node Degree；And

According to the description data, the Expressive Features of each POI clusters are obtained；And

Another aspect of the present invention includes there is provided a kind of equipment, the equipment：

One or more processors；

Storage device, for storing one or more programs,

When one or more of programs are by one or more of computing devices so that one or more of processing Device realizes that the POI on the one hand provided as described above recommends method.

Another aspect of the present invention is stored thereon with computer program there is provided a kind of computer-readable recording medium, the journey Realize that the POI on the one hand provided as described above recommends method when sequence is executed by processor.

As shown from the above technical solution, the embodiment of the present invention passes through according to acquired user mesh interested in the user POI visit capacity is marked, statistics visit capacity of the user to the target POI clusters belonging to the POI is obtained, and then according to described User has between the acquisition user couple and the target POI clusters and associated to the statistics visit capacity of the target POI clusters The transmission visit capacity of the association POI clusters of relation, enabling accessed according to the user the transmission of the association POI clusters Amount, obtains interest level data of the user to the association POI clusters, using the user individual number as the user According to, by by visit capacity of the user to single POI, being mapped to the visit capacity of the affiliated POI clusters of single POI, and then be delivered to The single affiliated POI clusters of POI have the visit capacity of the association POI clusters of incidence relation, to find sense of the user to association cluster Level of interest, and the visit capacity of user's POI clusters affiliated to single POI can be delivered to having not visited with single POI Affiliated POI clusters have the association POI clusters of incidence relation, effective user individual data are resulted in, therefore, it is possible to base In user individual data, accurately to user recommend POI that the user may be interested or it should be understood that POI so that Improve the success rate of POI recommendations.

In addition, using technical scheme provided by the present invention, by replacing single POI with cluster, realizing and utilizing cluster Global Information describes POI individual information, so as to enrich single POI information, can effectively improve the reliable of POI recommendations Property.

In addition, using technical scheme provided by the present invention, POI division is more accurate, will not be because of the passes of POI in itself Keyword, or loss of learning cause to divide indefinite or even mistake.

In addition, using technical scheme provided by the present invention, can be needed to select different levels according to different scenes, it is different The POI of semantic granularity.

In addition, using technical scheme provided by the present invention, being capable of significant increase Consumer's Experience.

【Brief description of the drawings】

Technical scheme in order to illustrate the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art In required for the accompanying drawing that uses be briefly described, it should be apparent that, drawings in the following description are some realities of the present invention Example is applied, for those of ordinary skill in the art, without having to pay creative labor, can also be attached according to these Figure obtains other accompanying drawings.

Fig. 1 recommends the schematic flow sheet of method for the POI that one embodiment of the invention is provided；

The structural representation for the POI recommendation apparatus that Fig. 2 provides for another embodiment of the present invention；

Fig. 3 is suitable for for the block diagram for the exemplary computer system/server 12 for realizing embodiment of the present invention.

【Embodiment】

To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is A part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art The whole other embodiments obtained under the premise of creative work is not made, belong to the scope of protection of the invention.

It should be noted that terminal involved in the embodiment of the present invention can include but is not limited to mobile phone, individual digital Assistant (Personal Digital Assistant, PDA), radio hand-held equipment, tablet personal computer (Tablet Computer), PC (Personal Computer, PC), MP3 player, MP4 players, wearable device (for example, intelligent glasses, Intelligent watch, Intelligent bracelet etc.) etc..

In addition, the terms "and/or", only a kind of incidence relation for describing affiliated partner, represents there may be Three kinds of relations, for example, A and/or B, can be represented：Individualism A, while there is A and B, these three situations of individualism B.Separately Outside, character "/" herein, it is a kind of relation of "or" to typically represent forward-backward correlation object.

Fig. 1 recommends the schematic flow sheet of method for the POI that one embodiment of the invention is provided, as shown in Figure 1.

101st, user target POI interested in user visit capacity is obtained.

102nd, according to visit capacity of the user to the target POI, the user is obtained to the target belonging to the POI The statistics visit capacity of POI clusters.

103rd, according to statistics visit capacity of the user to the target POI clusters, the user is obtained to association POI groups The transmission visit capacity of cluster, has incidence relation between the association POI clusters and the target POI clusters.

104th, according to transmission visit capacity of the user to the association POI clusters, the user is obtained to the association The interest level data of POI clusters, using the user individual data as the user.

It is understood that being visited due to user can be directly obtained the statistics of the target POI clusters belonging to the POI The amount of asking, then, then it can obtain institute directly according to statistics visit capacity of the user to the target POI clusters belonging to the POI Interest level data of the user to the target POI clusters are stated, using the user individual data as the user.

It should be noted that 101~104 executive agent can be partly or entirely the application for being located locally terminal, Or can also be the plug-in unit being arranged in the application of local terminal or SDK (Software Development Kit, SDK) etc. functional unit, either can also in network side server processing engine or Can also be the distributed system positioned at network side, the present embodiment is to this without being particularly limited to.

It is understood that the application can be mounted in the local program (nativeApp) in terminal, or may be used also To be a web page program (webApp) of browser in terminal, the present embodiment is to this without being particularly limited to.

So, by the visit capacity according to acquired user target POI interested in the user, the user is obtained To the statistics visit capacity of the target POI clusters belonging to the POI, and then according to system of the user to the target POI clusters Visit capacity is counted, obtaining the has incidence relation transmission for associating POI clusters between the user couple and the target POI clusters is visited The amount of asking, enabling according to transmission visit capacity of the user to the association POI clusters, obtains the user to the association The interest level data of POI clusters, using the user individual data as the user, by by user to single POI's Visit capacity, is mapped to the visit capacity of the affiliated POI clusters of single POI, and then is delivered to the affiliated POI clusters of single POI with pass The visit capacity of the association POI clusters of connection relation, to find interest level of the user to association cluster, and can be by user couple The visit capacity of the single affiliated POI clusters of POI be delivered to have not visited there is incidence relation with the affiliated POI clusters of single POI Associate POI clusters, result in effective user individual data, therefore, it is possible to based on user individual data, accurately to User recommend the user may POI interested or it should be understood that POI, so as to improve the success rate of POI recommendations.

Alternatively, in a possible implementation of the present embodiment, in 101, a variety of methods can be specifically used, Obtain user target POI interested.

During a concrete implementation, specifically the target can be obtained according to the attribute data of the user POI.For example, the attribute data of user is is 20 years old at the age, sex be female, resident address be Shangdi and hobby to stroll in the park, So, then the target POI can be obtained for Yuanmingyuan Park.

During another concrete implementation, specifically it can obtain described according to the nearest inquiry operation of the user Target POI.For example, user inquired about Yuanmingyuan Park within three days, then, then the target POI can be obtained for Yuanmingyuan Park.

During another concrete implementation, specifically it can obtain described according to the current inquiry operation of the user Target POI.For example, user currently inquires about Yuanmingyuan Park, then, then the target POI can be obtained for Yuanmingyuan Park.

During another concrete implementation, the position that can be specifically currently located according to the user obtains described Target POI.For example, the position that user is currently located is in Yuanmingyuan Park, then, then the target POI can be obtained for Yuanmingyuan Park.

So, can be according to the user behavior data of the user after user target POI interested is got, system User target POI interested in user visit capacity is counted out, for example, clicking on, browsing.

Alternatively, in a possible implementation of the present embodiment, before 102, it can further include structure Build the POI cluster set with tree structure.

During a concrete implementation, the user behavior data of the whole network user can be specifically obtained, and then, then can be with According to the user behavior data, the incidence relation between POI two-by-two is obtained.It is then possible to according between the POI two-by-two The relevant parameter of incidence relation between incidence relation and the POI two-by-two, using community discovery algorithm, is carried out at POI clusters Reason, to obtain at least one POI cluster with tree structure relation, for according to the target POI, obtaining the target Target POI clusters belonging to POI, and according to the target POI clusters, obtain having together between the target POI clusters The association POI clusters of matter incidence relation are homogeneity POI clusters.Wherein, at least one described POI cluster in each POI clusters At least one POI can be included.

So, by the prolonged behavior of a large number of users, the incidence relation between POI and POI is excavated, is closed by this Connection relation, the POI of all full doses is linked together to form POI network.Then, by using community discovery algorithm, Go to find the POI i.e. POI clusters of the good cluster cluster of cohesion in this network of throwing the net, POI clusters, which have, here disclosure satisfy that largely The feature of particular demands of the user under similar scene.This scheme is a kind of division of stratification, uses identical method pair Sorted POI, is divided again for the first time, it becomes possible to obtain the POI classification of bigger granularity.

In the implementation process, the relevant parameter of the incidence relation between the POI two-by-two used can be described two The support of incidence relation between two POI, or can also be the support of the incidence relation between the POI two-by-two and institute The cosine similarity of the incidence relation between POI two-by-two is stated, the present embodiment is to this without being particularly limited to.

Specifically, it is possible, firstly, to gather user behavior number of each user in the range of certain time in the whole network user According to for example, click behavioral data, retrieval behavioral data or positioning track data etc., obtain the incidence relation between POI two-by-two, together When, can also be further according to the user behavior data gathered, the association for obtaining the incidence relation between the POI two-by-two is joined Number is for example, the cosine of the incidence relation between the support and the POI two-by-two of incidence relation between the POI two-by-two is similar Degree.

The support of incidence relation between POI two-by-two, depending on user in the range of certain time consecutively or simultaneously, point Hit, retrieve or positioned the two POI number of times.

For example, a user in the range of certain time consecutively or simultaneously, click on, retrieve or positioned the two POI, that , the support of incidence relation can then increase by 1 between the two POI.

The cosine similarity of incidence relation between POI two-by-two, depending on the support of the incidence relation between POI two-by-two With the temperature of each POI in POI two-by-two.

For example, the cosine similarity of the incidence relation between POI can be two-by-two One user clicks on, retrieves or positioned this POI in the range of certain time, then, this POI temperature can then increase Plus 1.

The relevant parameter of incidence relation between incidence relation between POI two-by-two is obtained, and the POI two-by-two Afterwards, then the association between the POI two-by-two can be closed according to the relevant parameter of the incidence relation between the POI two-by-two It is to delete the incidence relation between the weaker POI two-by-two of incidence relation that system, which carries out filtration treatment,.

The incidence relation between the POI two-by-two will be used as using the support of the incidence relation between POI two-by-two below Exemplified by relevant parameter, how filtration treatment is carried out to the incidence relation between the POI two-by-two under explanation.

The support for the incidence relation that can be given between POI two-by-two, pre-sets two threshold values S1 and S2, and S2 is more than S1.Support is less than to the incidence relation between S1 POI two-by-two, directly deleted；Support is more than or equal to S2 two-by-two Incidence relation between POI, directly retains；It is more than or equal to S1 and the association being less than between S2 POI two-by-two for support Relation, then need further to be judged, to determine which can retain, and which needs to delete.For example, setting a threshold value again L, is more than or equal to S1 and the incidence relation being less than between S2 POI two-by-two for support, if the two POI and other POI Between incidence relation support be less than L, then the incidence relation between the two POI need retain；If the two POI and its The support of incidence relation between his POI is more than or equal to L, then the incidence relation between the two POI needs to delete.

In the incidence relation between filtering out POI two-by-two after weaker incidence relation, obtain a POI and POI and lead to Cross association relational organization into one using POI as node, incidence relation be side network.It is then possible to be calculated using community discovery Method, in the network obtained, finds the POI i.e. POI clusters of the more close cluster cluster of incidence relation.Calculated in community discovery In method, the every cluster POI data volume upper limit can be set for example, 25 etc..The result that first time is divided is as handling next time Base unit be to regard a new POI as, repeat aforesaid operations, it is possible to obtain the thicker POI division results of granularity.Until hair Now there is effective incidence relation between no any two POI clusters and terminate division.

So, the POI cluster set with tree structure is just constructed, the POI clusters in the POI cluster set are all tools There are the homogeneity POI clusters of homogeneity incidence relation.

The present invention is by according to acquired user target POI interested, obtaining the target belonging to the target POI POI clusters, and then according to the target POI clusters, obtain has the same of homogeneity incidence relation between the target POI clusters Matter POI clusters, enabling recommend the homogeneity POI clusters to the user, due to the excavation using homogeneity incidence relation, make The particular demands that each POI clusters divided disclosure satisfy that user are obtained, therefore, the based process unit recommended are suitable as, Accurately to user recommend the user may POI interested or it should be understood that POI so that improve POI recommendations into Power.

Alternatively, in a possible implementation of the present embodiment, the POI cluster collection with tree structure is being built After conjunction, in addition it is also necessary to further excavate and associating with isomery incidence relation between the POI clusters in the POI cluster set POI clusters are isomery POI clusters.

Specifically, it can specifically obtain between the POI two-by-two not in the tree structure under same specified node Incidence relation support, and then, then can be according to two under the specified node same not in the tree structure The pass between the POI two-by-two under incidence relation and the specified node same not in the tree structure between two POI The support of connection relation, carries out POI cluster isomery association process, to obtain the isomery incidence relation between POI clusters two-by-two, with For according to the target POI clusters, obtaining with associating POI clusters with isomery incidence relation between the target POI clusters That is isomery POI clusters.

So, the structure of isomery POI clusters is passed through, enabling the isomery incidence relation between POI is excavated, so as to reach Reduction Deta sparseness and the fine-grained purpose of lifting cluster are arrived.

In the implementation process, the specified node can be root node, or can also be other height under root node Node layer, the present embodiment is to this without being particularly limited to.

The support of incidence relation between POI two-by-two, specifically describes and may be referred to phase in an embodiment inside the Pass Hold, here is omitted.

The support for the incidence relation that can be given in advance between POI two-by-two, pre-sets a threshold value S3.Support is big In or equal to incidence relation between S3 POI two-by-two, be defined as each belonging to POI clusters between isomery incidence relation.

In the present invention, by according to acquired user target POI interested, obtaining the mesh belonging to the target POI POI clusters are marked, and then according to the target POI clusters, obtain has isomery incidence relation between the target POI clusters Isomery POI clusters, enabling recommend the isomery POI clusters to the user, due to the excavation using isomery incidence relation, The incidence relation between semanteme and POI therefore, it is possible to enrich POI, accurately recommends the user interested to user POI or it should be understood that POI so that improve POI recommendation success rate.

Alternatively, in a possible implementation of the present embodiment, the POI clusters with tree structure are being constructed After set, it can also further excavate the i.e. global homogeneity association of the association POI clusters in the overall situation between homogeneity POI clusters and close System.

Specifically, the description data of each POI clusters at least one described POI cluster can be specifically obtained, wherein, The description data can include but is not limited at least one in comment data and branding data.Then, then can be according to institute Description data are stated, the Expressive Features of each POI clusters are obtained, and then, according to the Expressive Features of each POI clusters, The processing of POI clusters global association is carried out, to obtain the global homogeneity incidence relation between POI clusters two-by-two, for according to described Target POI clusters, obtain the global homogeneity POI clusters between the target POI clusters with global homogeneity incidence relation.

So, by finding the homogeneity POI clusters in different zones (tree structure under i.e. different root nodes) in the overall situation Between similitude, excavate the homogeneity incidence relation between homogeneity POI clusters in the overall situation, and then by the use for being familiar with region Family preference is delivered to another strange region, can effectively improve the reliability of POI recommendations.

, can be based on constructed tree structure for the situation that description data are comment data in the implementation POI cluster set, obtain the comment data of each POI clusters, and then, then can carry out cutting word processing to these comment datas (including stop words processing), to obtain cutting word result.According to the statistical parameter of each cutting word result, for example, frequency of use etc., choosing Part cutting word result or whole cutting word results are selected as the Expressive Features of each POI clusters.It is special according to the description of each POI clusters Levy, calculate the similarity between POI clusters two-by-two, similarity is more than or equal to the similarity threshold M pre-set two-by-two POI clusters, are defined as having global homogeneity incidence relation；Similarity is less than to the similarity threshold M pre-set POI two-by-two Cluster, is defined as without global homogeneity incidence relation.

, can be based on constructed tree structure for the situation that description data are branding data in the implementation POI cluster set, obtain the branding data of each POI clusters, can specifically be obtained from POI attribute fields.And then, then may be used With these branding datas according to each POI clusters, the Expressive Features of each POI clusters are obtained.According to retouching for each POI clusters Feature is stated, the similarity between POI clusters two-by-two is calculated, by similarity more than or equal to the similarity threshold M's pre-set POI clusters, are defined as having global homogeneity incidence relation two-by-two；Similarity is less than the two of the similarity threshold M pre-set Two POI clusters, are defined as without global homogeneity incidence relation.

So, then can be according to user's after the homogeneity incidence relation in the overall situation between homogeneity POI clusters is excavated Position data, determines whether user is familiar with the region i.e. resident area of the user positioned at the user.User's is familiar with region, can Obtained with the attribute data of the operation behavior data according to user or user, method for digging of the prior art can be used, Here is omitted.

If what user was no longer at the user is familiar with region, can according to the target POI clusters, obtain with it is described There is the global homogeneity POI clusters of global homogeneity incidence relation between target POI clusters；If user is still located on the user's It is familiar with region, then without according to the target POI clusters, obtaining with having global homogeneity to associate between the target POI clusters The global homogeneity POI clusters of relation, and the constructed POI cluster set with tree structure is based on directly on, according to described Target POI clusters, will have the homogeneity POI clusters of homogeneity incidence relation, directly recommend use between the target POI clusters Family.

The position data of so-called user, refers to the position data of user's present position, is specifically as follows user institute The terminal used uses existing various location technologies, for example, global positioning system (Global Positioning System, GPS) technology, the positioning of Wireless Fidelity (Wireless Fidelity, Wi-Fi) location technology or architecture technology etc. Technology, the geographic position data of the positioning result of the terminal obtained, i.e. terminal position, the present embodiment to this without It is particularly limited to.

In the present invention, by according to acquired user target POI interested, obtaining the mesh belonging to the target POI Mark POI clusters, and then according to the target POI clusters and the position data of the user, obtain with the target POI clusters it Between there is the global homogeneity POI clusters of homogeneity incidence relation, enabling recommend the global homogeneity POI groups to the user Cluster, due to the excavation using global homogeneity incidence relation so that each POI clusters divided disclosure satisfy that the particular needs of user Ask, therefore, be suitable as the based process unit recommended, accurately to user recommend the user may POI interested or It should be understood that POI so that improve POI recommendation success rate.

Alternatively, in a possible implementation of the present embodiment, in 102, target POI clusters are conducted interviews During amount statistics, same user can be subjected to summation process to the statistics visit capacity of each POI in target POI clusters, Total visit capacity using the result of calculation of summation process as target POI clusters is statistics visit capacity.

Alternatively, in a possible implementation of the present embodiment, the incidence relation can include but is not limited to At least one of in following incidence relation：

Homogeneity incidence relation；

Isomery incidence relation；And

Global homogeneity incidence relation.

During a concrete implementation, for the situation that the incidence relation is homogeneity incidence relation, then, In 103, it can specifically obtain homogeneity and associate attenuation coefficient for example, 0.8；And then, then can be according to the user to the target The statistics visit capacity of POI clusters associates attenuation coefficient with the homogeneity, obtains transmission of the user to the association POI clusters Visit capacity, for example, the user is associated into multiplying for attenuation coefficient with stating homogeneity to the statistics visit capacity of the target POI clusters Product, is used as transmission visit capacity of the user to the association POI clusters.

During another concrete implementation, for the situation that the incidence relation is isomery incidence relation, then, In 103, it can specifically obtain isomery and associate attenuation coefficient for example, 0.75；And then, then can be according to the user to the target The statistics visit capacity of POI clusters associates attenuation coefficient with the isomery, obtains transmission of the user to the association POI clusters Visit capacity, for example, the user is associated into multiplying for attenuation coefficient with stating isomery to the statistics visit capacity of the target POI clusters Product, is used as transmission visit capacity of the user to the association POI clusters.As a rule, isomery association attenuation coefficient needs to set The homogeneity that is less than put associates attenuation coefficient.

During another concrete implementation, for the situation that the incidence relation is global homogeneity incidence relation, that , in 103, can specifically obtain statistics visit capacity of the user to other POI clusters, other described POI clusters and institute Stating between target POI clusters and the association POI clusters has global homogeneity incidence relation；And then, then it can be used according to described Family, to the statistics visit capacity of other POI clusters, obtains institute to the statistics visit capacity of the target POI clusters and the user Transmission visit capacity of the user to the association POI clusters is stated, for example, the user is visited the statistics of the target POI clusters The amount of asking and statistics visit capacity sum of the user to other POI clusters, as the user to the association POI clusters Transmission visit capacity.

Alternatively, in 104, specifically can be to the user couple in a possible implementation of the present embodiment The transmission visit capacity of the association POI clusters carries out data normalization processing, to obtain the user to the association POI clusters Interest level data.

During a concrete implementation, formula (1-q^n)/1-q can be specifically utilized, wherein, q=9/10, n is pass Join the transmission visit capacity of POI clusters, the user is carried out at data normalization to the transmission visit capacity of the association POI clusters Reason, produces interest level data of the user to the association POI clusters.

So, the user individual data that the interest level data can be based on, so that other business modules enter Row is called, for example, be associated the business module of the sequence of POI clusters etc..

In the present embodiment, by the visit capacity according to acquired user target POI interested in the user, institute is obtained Statistics visit capacity of the user to the target POI clusters belonging to the POI is stated, and then according to the user to the target POI groups The statistics visit capacity of cluster, obtains between the user couple and the target POI clusters and to associate POI clusters with incidence relation Transmit visit capacity, enabling according to transmission visit capacity of the user to the association POI clusters, obtain the user to institute State the interest level data of association POI clusters, using the user individual data as the user, by by user to single POI visit capacity, is mapped to the visit capacity of the affiliated POI clusters of single POI, and then is delivered to and single POI affiliated POI clusters tool The visit capacity of relevant association POI clusters, to find user to associating the interest level of cluster, and will can be used The visit capacity of family POI clusters affiliated to single POI be delivered to have not visited with the affiliated POI clusters of single POI have associate The association POI clusters of system, result in effective user individual data, therefore, it is possible to based on user individual data, accurately Ground to user recommend the user may POI interested or it should be understood that POI, so as to improve the success rate of POI recommendations.

It should be noted that for foregoing each method embodiment, in order to be briefly described, therefore it is all expressed as a series of Combination of actions, but those skilled in the art should know, the present invention is not limited by described sequence of movement because According to the present invention, some steps can be carried out sequentially or simultaneously using other.Secondly, those skilled in the art should also know Know, embodiment described in this description belongs to preferred embodiment, involved action and module is not necessarily of the invention It is necessary.

In the above-described embodiments, the description to each embodiment all emphasizes particularly on different fields, and does not have the portion being described in detail in some embodiment Point, it may refer to the associated description of other embodiment.

The structural representation for the POI recommendation apparatus that Fig. 2 provides for another embodiment of the present invention, as shown in Figure 2.The present embodiment POI recommendation apparatus can include acquiring unit 21, associative cell 22 and construction unit 23.Wherein, acquiring unit 21, for obtaining Take family target POI interested in user visit capacity；Associative cell 22, for according to the user to the target POI visit capacity, obtains statistics visit capacity of the user to the target POI clusters belonging to the POI；The associative cell 22, the statistics visit capacity to the target POI clusters according to the user is additionally operable to, the user is obtained to association POI clusters Transmission visit capacity, between the association POI clusters and the target POI clusters have incidence relation；Construction unit 23, is used for According to transmission visit capacity of the user to the association POI clusters, sense of the user to the association POI clusters is obtained emerging Interesting level data, using the user individual data as the user.

It should be noted that the POI recommendation apparatus that the present embodiment is provided can be partly or entirely to be located locally end The application of the terminal device on the vehicles is specified at end, or can also be to be arranged in inserting in the application of local terminal The functional unit such as part or SDK (Software Development Kit, SDK), or can also be positioned at net Processing engine in the server of network side, or can also be positioned at network side distributed system, the present embodiment to this without It is particularly limited to.

Alternatively, in a possible implementation of the present embodiment, the acquiring unit 21 specifically can be used for root According to the attribute data of the user, the target POI is obtained；Or according to the nearest inquiry operation of the user, obtain described Target POI；Or according to the current inquiry operation of the user, obtain the target POI；Or according to the current institute of the user Position, obtain the target POI.

Alternatively, in a possible implementation of the present embodiment, the associative cell 22 can also be used further In the user behavior data for obtaining the whole network user；According to the user behavior data, the incidence relation between POI two-by-two is obtained； And according to the relevant parameter of the incidence relation between the incidence relation between the POI two-by-two and the POI two-by-two, using society Area finds algorithm, carries out POI clustering processings, to obtain at least one POI cluster with tree structure relation, for according to institute State target POI, obtain the target POI clusters belonging to the target POI, and according to the target POI clusters, obtain with it is described The association POI clusters with homogeneity incidence relation are homogeneity POI clusters between target POI clusters.

Alternatively, in a possible implementation of the present embodiment, the associative cell 22 specifically can be used for root According to the relevant parameter of the incidence relation between the POI two-by-two, the incidence relation between the POI two-by-two is carried out at filtering Reason；And according to the incidence relation between the POI two-by-two after the filtration treatment, using community discovery algorithm, carry out POI and gather Class processing, to obtain at least one POI cluster with tree structure relation.

Alternatively, in a possible implementation of the present embodiment, the associative cell 22 can also be used further The support of incidence relation between the POI two-by-two not in the tree structure under same specified node is obtained；And According to the incidence relation between the POI two-by-two under the specified node same not in the tree structure and described not in institute The support of the incidence relation between the POI two-by-two in tree structure under same specified node is stated, POI clusters isomery is carried out and closes Connection processing, to obtain isomery incidence relation two-by-two between POI clusters, for according to the target POI clusters, obtain with it is described The association POI clusters with isomery incidence relation are isomery POI clusters between target POI clusters.

Alternatively, in a possible implementation of the present embodiment, the associative cell 22 can also be used further In the description data for obtaining each POI clusters at least one described POI cluster；The description data include comment data and product At least one of in board data；According to the description data, the Expressive Features of each POI clusters are obtained；And according to institute The Expressive Features of each POI clusters are stated, the processing of POI clusters global association is carried out, it is same to obtain the overall situation between POI clusters two-by-two Matter incidence relation, for according to the target POI clusters, obtaining with having global homogeneity to associate between the target POI clusters The association POI clusters of relation are global homogeneity POI clusters.

Homogeneity incidence relation；

Isomery incidence relation；And

Global homogeneity incidence relation.

During a concrete implementation, for the situation that the incidence relation is homogeneity incidence relation, then, it is described Associative cell 22, specifically can be used for obtaining homogeneity association attenuation coefficient for example, 0.8；And then, then can be according to the user couple The statistics visit capacity of the target POI clusters associates attenuation coefficient with the homogeneity, obtains the user to the association POI groups The transmission visit capacity of cluster, for example, the user is associated into decay system with stating homogeneity to the statistics visit capacity of the target POI clusters Several products, is used as transmission visit capacity of the user to the association POI clusters.

During another concrete implementation, for the situation that the incidence relation is isomery incidence relation, then, institute Associative cell 22 is stated, specifically can be used for obtaining isomery association attenuation coefficient for example, 0.75；And then, then it can be used according to described Family associates attenuation coefficient to the statistics visit capacity of the target POI clusters with the isomery, obtains the user to the association The transmission visit capacity of POI clusters, for example, the user is associated to the statistics visit capacity of the target POI clusters with stating isomery The product of attenuation coefficient, is used as transmission visit capacity of the user to the association POI clusters.As a rule, isomery association declines Subtracting coefficient needs the homogeneity that is less than set to associate attenuation coefficient.

During another concrete implementation, for the situation that the incidence relation is global homogeneity incidence relation, that , the associative cell 22 specifically can be used for obtaining statistics visit capacity of the user to other POI clusters, it is described other There is global homogeneity incidence relation between POI clusters and the target POI clusters and the association POI clusters；And then, then can be with The statistics visit capacity of the target POI clusters and the user are visited the statistics of other POI clusters according to the user The amount of asking, obtains transmission visit capacity of the user to the association POI clusters, for example, by the user to the target POI groups The statistics visit capacity of cluster and statistics visit capacity sum of the user to other POI clusters, as the user to described Associate the transmission visit capacity of POI clusters.

It should be noted that method in the corresponding embodiments of Fig. 1, the POI recommendation apparatus that can be provided by the present embodiment is real It is existing.The related content that may refer in the corresponding embodiments of Fig. 1 is described in detail, here is omitted.

In the present embodiment, pass through user of the associative cell according to acquired in acquiring unit target interested in the user POI visit capacity, obtains statistics visit capacity of the user to the target POI clusters belonging to the POI, and then use according to described Family has between the acquisition user couple and the target POI clusters and associated to the statistics visit capacity of the target POI clusters The transmission visit capacity of the association POI clusters of system so that construction unit can be according to biography of the user to the association POI clusters Visit capacity is passed, interest level data of the user to the association POI clusters are obtained, using the user as the user Property data, by by visit capacity of the user to single POI, being mapped to the visit capacity of the affiliated POI clusters of single POI, Jin Erchuan The visit capacity for associating POI clusters that there is incidence relation with the affiliated POI clusters of single POI is delivered to, to find user to association group The interest level of cluster, and the visit capacity of user's POI clusters affiliated to single POI can be delivered to have not visited with list The individual affiliated POI clusters of POI have the association POI clusters of incidence relation, result in effective user individual data, therefore, User individual data can be based on, accurately to user recommend the user may POI interested or it should be understood that POI, so as to improve the success rate of POI recommendations.

Fig. 3 shows the block diagram suitable for being used for the exemplary computer system/server 12 for realizing embodiment of the present invention. The computer system/server 12 that Fig. 3 is shown is only an example, to the function of the embodiment of the present invention and should not use scope Bring any limitation.

As shown in figure 3, computer system/server 12 is showed in the form of universal computing device.Computer system/service The component of device 12 can include but is not limited to：One or more processor or processing unit 16, storage device or system Memory 28, the bus 18 of connection different system component (including system storage 28 and processing unit 16).

Bus 18 represents the one or more in a few class bus structures, including memory bus or Memory Controller, Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.Lift For example, these architectures include but is not limited to industry standard architecture (ISA) bus, MCA (MAC) Bus, enhanced isa bus, VESA's (VESA) local bus and periphery component interconnection (PCI) bus.

Computer system/server 12 typically comprises various computing systems computer-readable recording medium.These media can be appointed What usable medium that can be accessed by computer system/server 12, including volatibility and non-volatile media, it is moveable and Immovable medium.

System storage 28 can include the computer system readable media of form of volatile memory, such as arbitrary access Memory (RAM) 30 and/or cache memory 32.Computer system/server 12 may further include other removable Dynamic/immovable, volatile/non-volatile computer system storage medium.Only as an example, storage system 34 can be used for Read and write immovable, non-volatile magnetic media (Fig. 3 is not shown, is commonly referred to as " hard disk drive ").Although not showing in Fig. 3 Going out, can providing for the disc driver to may move non-volatile magnetic disk (such as " floppy disk ") read-write, and to removable The CD drive of anonvolatile optical disk (such as CD-ROM, DVD-ROM or other optical mediums) read-write.In these cases, Each driver can be connected by one or more data media interfaces with bus 18.System storage 28 can be included extremely A few program product, the program product has one group of (for example, at least one) program module, and these program modules are configured to Perform the function of various embodiments of the present invention.

Program/utility 40 with one group of (at least one) program module 42, can be stored in such as system storage In device 28, such program module 42 includes --- but being not limited to --- operating system, one or more application program, other The realization of network environment is potentially included in each or certain combination in program module and routine data, these examples.Journey Sequence module 42 generally performs function and/or method in embodiment described in the invention.

Computer system/server 12 can also be with one or more external equipments 14 (such as keyboard, sensing equipment, aobvious Show device 24 etc.) communicate, the equipment that can also enable a user to interact with the computer system/server 12 with one or more is led to Letter, and/or any set with make it that the computer system/server 12 communicated with one or more of the other computing device Standby (such as network interface card, modem etc.) communication.This communication can be carried out by input/output (I/O) interface 44.And And, computer system/server 12 can also pass through network adapter 20 and one or more network (such as LAN (LAN), wide area network (WAN) and/or public network, such as internet) communication.As illustrated, network adapter 20 passes through bus 18 communicate with other modules of computer system/server 12.It should be understood that although not shown in the drawings, computer can be combined Systems/servers 12 use other hardware and/or software module, include but is not limited to：Microcode, device driver, at redundancy Manage unit, external disk drive array, RAID system, tape drive and data backup storage system etc..

Processing unit 16 is stored in program in system storage 28 by operation, thus perform various function application and Data processing, for example, realize that the POI that the embodiment corresponding to Fig. 1 is provided recommends method.

Another embodiment of the present invention additionally provides a kind of computer-readable recording medium, is stored thereon with computer program, The program realizes that the POI that the embodiment corresponding to Fig. 1 is provided recommends method when being executed by processor.

Specifically, any combination of one or more computer-readable media can be used.Computer-readable medium Can be computer-readable signal media or computer-readable recording medium.Computer-readable recording medium for example can be with System, device or the device of --- but being not limited to --- electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, or it is any more than Combination.The more specifically example (non exhaustive list) of computer-readable recording medium includes：With one or more wires Electrical connection, portable computer diskette, hard disk, random access memory (RAM), read-only storage (ROM), erasable type can compile Journey read-only storage (EPROM or flash memory), optical fiber, portable compact disc read-only storage (CD-ROM), light storage device, magnetic Memory device or above-mentioned any appropriate combination.In this document, computer-readable recording medium can be any includes Or the tangible medium of storage program, the program can be commanded execution system, device or device using or in connection make With.

Computer-readable signal media can be included in a base band or as the data-signal of carrier wave part propagation, Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including --- but It is not limited to --- electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be Any computer-readable medium beyond computer-readable recording medium, the computer-readable medium can send, propagate or Transmit for being used or program in connection by instruction execution system, device or device.

The program code included on computer-readable medium can be transmitted with any appropriate medium, including --- but do not limit In --- wireless, electric wire, optical cable, RF etc., or above-mentioned any appropriate combination.

It can be write with one or more programming languages or its combination for performing the computer that the present invention is operated Program code, described program design language includes object oriented program language-such as Java, Smalltalk, C++, Also include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with Fully perform, partly perform on the user computer on the user computer, as independent software kit execution, a portion Divide part execution or the execution completely on remote computer or server on the remote computer on the user computer. Be related in the situation of remote computer, remote computer can be by the network of any kind --- including LAN (LAN) or Wide area network (WAN) --- subscriber computer is connected to, or, it may be connected to outer computer (for example utilizes Internet service Provider comes by Internet connection).

It is apparent to those skilled in the art that, for convenience and simplicity of description, the system of foregoing description, The specific work process of device and unit, may be referred to the corresponding process in preceding method embodiment, will not be repeated here.

In several embodiments provided by the present invention, it should be understood that disclosed system, apparatus and method can be with Realize by another way.For example, device embodiment described above is only schematical, for example, the unit Divide, only a kind of division of logic function there can be other dividing mode when actually realizing, for example, multiple units or group Part can combine or be desirably integrated into another system, or some features can be ignored, or not perform.It is another, it is shown Or the coupling each other discussed or direct-coupling or communication connection can be by some interfaces, device or unit it is indirect Coupling is communicated to connect, and can be electrical, machinery or other forms.

The unit illustrated as separating component can be or may not be it is physically separate, it is aobvious as unit The part shown can be or may not be physical location, you can with positioned at a place, or can also be distributed to multiple On NE.Some or all of unit therein can be selected to realize the mesh of this embodiment scheme according to the actual needs 's.

In addition, each functional unit in each embodiment of the invention can be integrated in a processing unit, can also That unit is individually physically present, can also two or more units it is integrated in a unit.Above-mentioned integrated list Member can both be realized in the form of hardware, it would however also be possible to employ hardware adds the form of SFU software functional unit to realize.

Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those within the art that：It still may be used To be modified to the technical scheme described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic； And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical scheme spirit and Scope.

Claims

1. a kind of POI recommends method, it is characterised in that including：

Obtain user target POI interested in user visit capacity；

According to visit capacity of the user to the target POI, the user is obtained to the target POI clusters belonging to the POI Statistics visit capacity；

According to statistics visit capacity of the user to the target POI clusters, transmission of the user to association POI clusters is obtained Visit capacity, has incidence relation between the association POI clusters and the target POI clusters；

According to transmission visit capacity of the user to the association POI clusters, the user is obtained to the association POI clusters Interest level data, using the user individual data as the user.

2. according to the method described in claim 1, it is characterised in that the incidence relation is included in following incidence relation at least One：

Homogeneity incidence relation；

Isomery incidence relation；And

Global homogeneity incidence relation.

3. according to the method described in claim 1, it is characterised in that the incidence relation is homogeneity incidence relation；The basis The user obtains transmission visit capacity of the user to association POI clusters to the statistics visit capacity of the target POI clusters, Including：

Obtain homogeneity association attenuation coefficient；

Attenuation coefficient is associated to the statistics visit capacity of the target POI clusters with the homogeneity according to the user, obtains described Transmission visit capacity of the user to the association POI clusters.

4. according to the method described in claim 1, it is characterised in that the incidence relation is isomery incidence relation；The basis The user obtains transmission visit capacity of the user to association POI clusters to the statistics visit capacity of the target POI clusters, Including：

Obtain isomery association attenuation coefficient；

Attenuation coefficient is associated to the statistics visit capacity of the target POI clusters with the isomery according to the user, obtains described Transmission visit capacity of the user to the association POI clusters.

5. according to the method described in claim 1, it is characterised in that the incidence relation is global homogeneity incidence relation；It is described According to statistics visit capacity of the user to the target POI clusters, obtain the user and the transmission for associating POI clusters is accessed Amount, including：

Obtain statistics visit capacity of the user to other POI clusters, other described POI clusters and the target POI clusters and There is global homogeneity incidence relation between the association POI clusters；

According to the user to the system of the statistics visit capacities of the target POI clusters and the user to other POI clusters Visit capacity is counted, transmission visit capacity of the user to the association POI clusters is obtained.

6. according to the method described in claim 1, it is characterised in that described to obtain user target POI interested, including：

According to the attribute data of the user, the target POI is obtained；Or

7. the method according to claim 1~6 any claim, it is characterised in that it is described according to the user to institute Target POI visit capacity is stated, before obtaining the user to the statistics visit capacity of the target POI clusters belonging to the POI, is also wrapped Include：

Obtain the user behavior data of the whole network user；

According to the relevant parameter of the incidence relation between the incidence relation between the POI two-by-two and the POI two-by-two, using society Area finds algorithm, carries out POI clustering processings, to obtain at least one POI cluster with tree structure relation, for according to institute State target POI, obtain the target POI clusters belonging to the target POI, and according to the target POI clusters, obtain with it is described There is the POI clusters of homogeneity incidence relation between target POI clusters.

8. method according to claim 7, it is characterised in that the relevant parameter of the incidence relation between the POI two-by-two, Including：

The support of incidence relation between the POI two-by-two；Or

The cosine similarity of incidence relation between the support of incidence relation between the POI two-by-two and the POI two-by-two.

9. method according to claim 7, it is characterised in that incidence relation described in the basis two-by-two between POI and The relevant parameter of incidence relation between the POI two-by-two, using community discovery algorithm, carries out POI clustering processings, to be had There is at least one POI cluster of tree structure relation, including：

According to the relevant parameter of the incidence relation between the POI two-by-two, the incidence relation between the POI two-by-two was carried out Filter is handled；

According to the incidence relation between the POI two-by-two after the filtration treatment, using community discovery algorithm, POI clusters are carried out Processing, to obtain at least one POI cluster with tree structure relation.

10. method according to claim 7, it is characterised in that incidence relation described in the basis two-by-two between POI and The relevant parameter of incidence relation between the POI two-by-two, using community discovery algorithm, carries out POI clustering processings, to be had After having at least one POI cluster of tree structure relation, in addition to：

Obtain the support of the incidence relation between the POI two-by-two not in the tree structure under same specified node；

According to the incidence relation between the POI two-by-two under the specified node same not in the tree structure and it is described not The support of the incidence relation between POI two-by-two in the tree structure under same specified node, carries out POI clusters different Structure association process, to obtain isomery incidence relation two-by-two between POI clusters, for according to the target POI clusters, obtain with There is the association POI clusters of isomery incidence relation between the target POI clusters.

11. method according to claim 7, it is characterised in that incidence relation described in the basis two-by-two between POI and The relevant parameter of incidence relation between the POI two-by-two, using community discovery algorithm, carries out POI clustering processings, to be had After having at least one POI cluster of tree structure relation, in addition to：

Obtain the description data of each POI clusters at least one described POI cluster；It is described description data include comment data and At least one of in branding data；

According to the Expressive Features of each POI clusters, carry out the processing of POI clusters global association, with obtain two-by-two POI clusters it Between global homogeneity incidence relation, for according to the target POI clusters, obtain have between the target POI clusters it is complete The association POI clusters of office's homogeneity incidence relation.

12. a kind of POI recommendation apparatus, it is characterised in that including：

Associative cell, for according to visit capacity of the user to the target POI, obtaining the user to belonging to the POI Target POI clusters statistics visit capacity；

The associative cell, is additionally operable to the statistics visit capacity to the target POI clusters according to the user, obtains the user Transmission visit capacity to associating POI clusters, has incidence relation between the association POI clusters and the target POI clusters；

Construction unit, for according to transmission visit capacity of the user to the association POI clusters, obtaining the user to described The interest level data of POI clusters are associated, using the user individual data as the user.

13. device according to claim 12, it is characterised in that the incidence relation is included in following incidence relation extremely One item missing：

Homogeneity incidence relation；

Isomery incidence relation；And

Global homogeneity incidence relation.

14. device according to claim 12, it is characterised in that the incidence relation is homogeneity incidence relation；It is described to close Receipts or other documents in duplicate member, specifically for

Obtain homogeneity association attenuation coefficient；And

15. device according to claim 12, it is characterised in that the incidence relation is isomery incidence relation；It is described to close Receipts or other documents in duplicate member, specifically for

Obtain isomery association attenuation coefficient；And

16. device according to claim 12, it is characterised in that the incidence relation is global homogeneity incidence relation；Institute Associative cell is stated, specifically for

Obtain statistics visit capacity of the user to other POI clusters, other described POI clusters and the target POI clusters and There is global homogeneity incidence relation between the association POI clusters；And

17. device according to claim 12, it is characterised in that the acquiring unit, specifically

According to the attribute data of the user, the target POI is obtained；Or

18. the device according to claim 12~17 any claim, it is characterised in that the associative cell, is also used In

Obtain the user behavior data of the whole network user；

19. device according to claim 18, it is characterised in that the association ginseng of the incidence relation between the POI two-by-two Number, including：

The support of incidence relation between the POI two-by-two；Or

20. device according to claim 18, it is characterised in that the associative cell, specifically for

21. device according to claim 18, it is characterised in that the associative cell, is additionally operable to

Obtain the support of the incidence relation between the POI two-by-two not in the tree structure under same specified node；With And

22. device according to claim 18, it is characterised in that the associative cell, is additionally operable to

23. a kind of equipment, it is characterised in that the equipment includes：

One or more processors；

Storage device, for storing one or more programs,

When one or more of programs are by one or more of computing devices so that one or more of processors are real The existing method as described in any in claim 1~11.

24. a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the program is by processor The method as described in any in claim 1~11 is realized during execution.