CN103034508A - Software recommending method and software recommending system - Google Patents
Software recommending method and software recommending system Download PDFInfo
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Abstract
A software recommending method comprises the following steps of creating a feature library of a user group through a server, wherein the feature library of the user group stores types of the user group and the corresponding user group judgment feature; extracting feature information of a user through a client side, and submitting the feature information of the user to the server; matching the feature information of the user with the user feature judgment feature in the user group feature library through the server, and determining the user group type to which the user belongs; and recommending a software to the user through the server according to the determined user group type. According to the software recommending method, the software corresponding to the user group type to which the user belongs is recommended to the user according to the feature information of the user, so that the possibility of the user for receiving the recommended software can be improved, and the software recommending success rate can be improved. In addition, the invention also provided a software recommending system.
Description
[technical field]
The present invention relates to field of computer technology, particularly a kind of software recommendation method and system.
[background technology]
Along with the development of computer technology, client software is more and more extensive, spreads all over each classes such as instant messaging, audio frequency and video broadcast, resource downloading, web page browsing, input method, system supplymentary.The new software of client emerges in an endless stream, and the software business man wishes new software is pushed to the user on the one hand, and the user also wishes to touch the new software of oneself liking on the other hand, so produced the demand that software is recommended.
Traditional software way of recommendation is to recommend and the similar or similar software of mounted software to the user.Such as, the user has downloaded a player software, and system can recommend another player software again.And in fact, for this class software of player, the user often only needs to install a getting final product.So this software is recommended the user is had little significance, and do not provide its required or interested new software to the user, thereby the user accepts to recommend the success ratio of software also not high.
[summary of the invention]
Based on this, be necessary to provide a kind of and can improve the software recommendation method that software is recommended success ratio.
A kind of software recommendation method may further comprise the steps:
Server creates the customer group feature database, has stored customer group classification and corresponding customer group differentiation feature in the described customer group feature database;
Client is extracted user's characteristic information, and described user's characteristic information is submitted to described server;
Described server mates described user's characteristic information and the differentiation of the customer group in described customer group feature database feature, determines the customer group classification that described user is affiliated;
Described server is recommended software according to described definite customer group classification to described user.
Preferably, the step of described server establishment customer group feature database comprises:
Choose sample of users;
Described sample of users is classified, obtain the initial category of described sample of users;
Obtain the characteristic information of described sample of users;
Characteristic information according to described initial category and user creates the customer group feature database.
Preferably, the described step of obtaining the characteristic information of sample of users is:
Obtain the user profile of sample of users, extract characteristic information relevant with software in the described user profile;
The step that described characteristic information according to described initial category and user creates the customer group feature database is:
According to described initial category described user's characteristic information is carried out cluster analysis;
Obtain the customer group differentiation feature of customer group classification, correspondence and the reference value that described customer group is differentiated feature according to cluster analysis result;
The reference value of described customer group classification, corresponding customer group being differentiated feature and described customer group differentiation feature is stored in the customer group feature database.
Preferably, described server mates described user's characteristic information and the differentiation of the customer group in described customer group feature database feature, determines that the step of the customer group classification that described user is affiliated is:
Obtain the eigenwert in described user's the characteristic information;
Calculate the not corresponding described customer group of described eigenwert and all types of user realm and differentiate distance between the reference value of feature;
Obtain minimum customer group classification corresponding to described distance, be the customer group classification under the described user.
Preferably, described method also comprises:
The software recommendation list that described Servers installed is corresponding with described customer group classification;
Described server recommends the step of software to be according to described definite customer group classification to described user:
Choose corresponding software recommendation list according to described definite customer group classification;
It is uninstalled or recommend described user than the software of the software version update of user installation to filter out described user from described software recommendation list.
In addition, also be necessary to provide a kind of and can improve the software commending system that software is recommended success ratio.
A kind of software commending system comprises server and the client of carrying out data interaction with described server, and described server comprises:
Creation module is used for creating the customer group feature database, has stored customer group classification and corresponding customer group differentiation feature in the described customer group feature database;
Described client comprises: information extraction modules is used for extracting user's characteristic information, and described user's characteristic information is submitted to described server;
Described server also comprises:
Matching module is used for described user's characteristic information and the customer group differentiation feature of described customer group feature database are mated, and determines the customer group classification that described user is affiliated;
Recommending module is used for recommending software according to described definite customer group classification to described user.
Preferably, described creation module comprises:
Sample is chosen module, is used for choosing sample of users;
The preliminary classification module is used for described sample of users is classified, and obtains the initial category of described sample of users;
The characteristic information acquisition module is for the characteristic information that obtains described sample of users;
Feature database creates submodule, is used for creating the customer group feature database according to described initial category and user's characteristic information.
Preferably, described characteristic information acquisition module also is used for obtaining the user profile of described sample of users, extracts characteristic information relevant with software in the described user profile;
Described feature database creates submodule and comprises:
The cluster analysis module is used for according to described initial category described user's characteristic information being carried out cluster analysis;
Customer group classification acquisition module is used for obtaining the customer group differentiation feature of customer group classification, correspondence and the reference value that described customer group is differentiated feature according to cluster analysis result;
The customer group feature database is used for storing the customer group differentiation feature of described customer group classification, correspondence and the reference value that described customer group is differentiated feature.Preferably, described matching module comprises:
The eigenwert acquisition module is for the eigenwert of the characteristic information that obtains described user;
Distance calculation module is for the distance between the reference value of calculating described eigenwert and all kinds of described customer group differentiation features;
The class of subscriber determination module is used for obtaining minimum customer group classification corresponding to described distance, is the customer group classification under the described user.
Preferably, described server also comprises:
Module is set, is used for arranging the software recommendation list corresponding with described customer group classification;
Described recommending module comprises:
Module is chosen in tabulation, is used for choosing corresponding software recommendation list according to described definite customer group classification;
The screening module, it is uninstalled or recommend described user than the software of the software version update of user installation to be used for filtering out described user from described software recommendation list.
Above-mentioned software recommendation method and system, characteristic information by user that client is extracted mates with the differentiation of the customer group in the customer group feature database in server feature, determine the customer group classification that the user is affiliated, then recommend the software corresponding with the customer group classification to the user.Because the interested software of different customer group classification is different, recommend software corresponding to customer group classification under it according to user's characteristic information to the user, can improve the possibility that the user accepts to recommend software, thereby improve the success ratio that software is recommended.
[description of drawings]
Fig. 1 is the schematic flow sheet of a software recommendation method among the embodiment;
Fig. 2 is the schematic flow sheet of an establishment customer group feature database among the embodiment;
Fig. 3 is the schematic flow sheet of the establishment customer group feature database among another embodiment;
Fig. 4 is the schematic flow sheet of the affiliated customer group classification of definite user among the embodiment;
Fig. 5 is schematic flow sheet from the customer group classification of determining to the user that recommend software among the embodiment according to;
Fig. 6 is the structural representation of a software commending system among the embodiment;
Fig. 7 is the structural representation of a creation module among the embodiment;
Fig. 8 is the structural representation that a feature database among the embodiment creates submodule;
Fig. 9 is the structural representation of a matching module among the embodiment;
Figure 10 is the structural representation of the software commending system among another embodiment.
[embodiment]
As shown in Figure 1, in one embodiment, a kind of software recommendation method may further comprise the steps:
Step S10, server creates the customer group feature database, has stored customer group classification and corresponding customer group differentiation feature in the customer group feature database.
The customer group classification is to a large amount of users rear resulting plurality of classes of classifying, such as game user class, User class etc.Customer group differentiation feature refers to the feature for the customer group classification under the differentiation user, for example, the differentiation feature of User class can be the online class software installed number, installation amusement class software number, installation study class software number, use the time period of software etc.
Step S20, client is extracted user's characteristic information, and user's characteristic information is submitted to server.
Use in the process of various softwares the user, client records user's raw information is such as the upgrading frequency of the frequency of utilization of the set-up time that fills software matrix, software, software, software, user's browsing page record etc.Client is characteristic information extraction from the raw information of record, and the characteristic information that extracts can be used for determining the affiliated customer group classification of user.Concrete, characteristic information comprises all kinds of software numbers, software set-up time, all kinds of software application frequency, all kinds of software upgrading frequency, all kinds of web page browsing durations etc., wherein, the classification of software can be online class, amusement class, game class, study class etc.
In one embodiment, can from user's raw information, be extracted for the characteristic information of determining customer group classification under the user by client, and the characteristic information that extracts is submitted to server.In another embodiment, client also can be directly be submitted to server with user's raw information, by server characteristic information extraction from user's raw information.
Step S30, server mates user's characteristic information and the differentiation of the customer group in customer group feature database feature, determines the customer group classification that the user is affiliated.
Concrete, server obtains the customer group of mating most with user's characteristic information and differentiates feature, and then customer group classification corresponding to this differentiation feature is the affiliated customer group classification of user.
Step S40, server is recommended software according to the customer group classification of determining to the user.
Different customer group classifications can arrange recommends different software, and for example, for the game user class, then recommended games class software for the office user class, is then recommended office class software.
Above-mentioned software recommendation method, characteristic information by user that client is extracted mates with the differentiation of the customer group in the customer group feature database in server feature, determine the customer group classification that the user is affiliated, then recommend the software corresponding with the customer group classification to the user.Because the interested software of different customer group classification is different, recommend software corresponding to customer group classification under it according to user's characteristic information to the user, can improve the possibility that the user accepts to recommend software, thereby improve the success ratio that software is recommended.
As shown in Figure 2, in one embodiment, the detailed process that server creates the customer group feature database is:
Step S110 chooses sample of users.
Server from the storage user data the selected part user as sample of users, the sample of users of choosing relates to different classifications as far as possible, such as network bar users class, User class, child user class, the elderly's user class, software development user class, game user class etc.
Step S120 classifies to sample of users, obtains the initial category of sample of users.
Step S130 obtains the characteristic information of sample of users.
The user data of storing in the server comprises user's raw information, therefore need from raw information, to extract user's characteristic information, comprise all kinds of software numbers, software set-up time, all kinds of software application frequency, all kinds of software upgrading frequency, all kinds of web page browsing durations etc.
Step S140 is according to initial category and user's characteristic information establishment customer group feature database.
As shown in Figure 3, in another embodiment, the detailed process that server creates the customer group feature database is:
Step S101 chooses sample of users.
Step S102 classifies to sample of users, obtains the initial category of sample of users.
Step S103 obtains the user profile of sample of users, extracts the characteristic information relevant with software in the user profile.
User profile is user's raw information, from user's raw information, extract the characteristic information relevant with software, concrete, can be by traditional Euclidean distance tolerance, by the probability metrics criterion, with the divergence criterion function, combine to extract the characteristic information relevant with software based in the methods such as differentiation entropy minimization one or more.Browse duration etc. such as what the characteristic information relevant with software that extracts comprised the upgrading frequency of the frequency of utilization of the set-up time of the number of all kinds of softwares of installation, all kinds of softwares, all kinds of softwares, all kinds of softwares and all kinds of webpages.
Step S104 carries out cluster analysis according to initial category to user's characteristic information.
Concrete, can adopt traditional C mean algorithm, ISODATA algorithm, combine the characteristic information to the user to carry out cluster analysis based on any one or a few method in the methods such as the similarity measurement algorithm of sample and nuclear, neighbour's Function Criterion algorithm.
Step S105 obtains the customer group differentiation feature of customer group classification, correspondence and the reference value that customer group is differentiated feature according to cluster analysis result.
If for the some features in the characteristic information, the corresponding eigenwert of all sample of users of a certain initial category is all more approaching, and the corresponding eigenwert with the sample of users of other initial category differs greatly, illustrate that this feature not only has cohesion to this customer group, can also this customer group and other customer group be separated by this feature, then this is characterized as the differentiation feature of this customer group.Opposite, if for the sample of users of some initial category, there is very big-difference in the eigenwert of the some features in its characteristic information, and then other differentiates feature to this feature as this initial user realm.For example in 100 features of game user class, there is very large difference in the eigenwert of this feature of installation number of tool-class software, and then this feature of installation number of tool-class software is not as the differentiation feature of game user class.
Further, can determine according to the corresponding eigenwert of all sample of users of this initial category the reference value of this differentiation feature.Concrete can get with this differentiations feature accordingly the mean value of all eigenwerts or the mode in all eigenwerts as the reference value of this differentiation feature of this customer group.
Further, determine final customer group classification, with inappropriate preliminary classification deletion or merging.If for each feature in the characteristic information, all there is very large difference between the sample of users eigenwert of a certain initial category, illustrate that the division of this initial category is nonsensical, then with this initial category deletion; If the differentiation feature of certain two initial category is roughly the same, and each differentiation feature characteristic of correspondence value is also roughly the same, and then wherein any one initial category merges in another initial category.Thereby determine final customer group classification in the customer group feature database.
Step S106, the reference value of the customer group classification of obtaining, corresponding customer group being differentiated feature and customer group differentiation feature is stored in the customer group feature database.
As shown in Figure 4, in one embodiment, the detailed process of step S30 is:
Step S302 obtains the eigenwert in user's the characteristic information.
For example, certain user's characteristic information is: 5 sections of online class softwares, online class software application frequency 1 hour/day, 3 sections of amusement class softwares, amusement class software application frequency 2 hours/day, 2 sections of tool-class softwares, tool-class software application frequency 0.5 hour/day, but the eigenwert 5,1,3,2,2,0.5 in the characteristic information extraction then.Further, the eigenwert of extracting can be formed proper vector.As above in the example eigenwert is formed proper vector and be (5,1,3,2,2,0.5).
Step S304, the not corresponding customer group of computation of characteristic values and all types of user realm is differentiated the distance between the reference value of feature.
In one embodiment, calculate Euclidean distance between user's eigenwert and the reference value that all types of user group differentiates feature.Concrete, as can be respectively the eigenwert obtained among the step S302 and customer group to be differentiated feature reference value composition characteristic vector, and calculate two Euclidean distances between the proper vector.
In addition, when the dimension of user characteristics value vector during greater than the dimension of the reference vectors of the differentiation feature of some customer group classifications, then take the reference vectors of differentiating feature as benchmark, reject the differentiation feature reference value redundant character value in addition in the user characteristics value vector, so that user characteristics value vector is consistent with differentiation feature reference vectors dimension, calculate again Euclidean distance between the two.
Step S306 obtains customer group classification corresponding to minor increment, is the customer group classification under the user.
The distance of the reference value of user's eigenwert and customer group differentiation feature is less, illustrate that then this user customer group classification corresponding with this differentiation feature is more approaching, obtain both customer group classifications corresponding apart from the customer group differentiation feature of minimum and be the customer group classification under the user.
In another embodiment, above-mentioned software recommendation method also comprises step: the software recommendation list that Servers installed is corresponding with the customer group classification.Concrete, corresponding software recommendation list can be set according to the differentiation feature of each customer group classification, and in server, store the mapping table of customer group classification and software recommendation list.For example, for the game class user, the software recommendation list can be set comprise: QQ driving, hero kill, QQ three states etc.The software recommendation list that so arranges more can satisfy user's potential demand, improves the user and accepts to recommend the probability of software, thereby further improve the success ratio that software is recommended.
As shown in Figure 5, in one embodiment, the detailed process of above-mentioned steps S40 is:
Step S402 chooses corresponding software recommendation list according to the customer group classification of determining.
According to the customer group classification under the user, in the mapping table of customer group classification and software recommendation list, find corresponding software recommendation list.
Step S404, it is uninstalled or recommend the user than the software of the software version update of user installation to filter out the user from the software recommendation list.
Concrete, characteristic information according to the user can obtain the current software matrix of having installed of user, software matrix and the software recommendation list of having installed are compared, can obtain the uninstalled software of user in the software recommendation list and than the software of the mounted software version update of user, then more recommend the user with the uninstalled software of user in the software recommendation list that obtains and than the software of the mounted software version update of user.In addition, client shows that the user can select corresponding software to download and install after receiving the software of server recommendation.
As shown in Figure 6, in one embodiment, a kind of software commending system comprises server 100 and the client 200 of carrying out data interaction with server 100; Server 100 comprises creation module 110, matching module 120 and recommending module 130; Client 200 comprises information extraction modules 210; Wherein:
Creation module 110 is used for creating the customer group feature database, has stored customer group classification and corresponding customer group differentiation feature in the customer group feature database.
The customer group classification is to a large amount of users rear resulting plurality of classes of classifying, such as game user class, User class etc.Customer group differentiation feature refers to the feature for the customer group classification under the differentiation user, for example, the differentiation feature of User class can be the online class software installed number, installation amusement class software number, installation study class software number, use the time period of software etc.
Information extraction modules 210 is used for extracting user's characteristic information, and user's characteristic information is submitted to server 100.
Use in the process of various softwares the user, the raw information of client 200 recording users is such as the upgrading frequency of the frequency of utilization of the set-up time that fills software matrix, software, software, software, user's browsing page record etc.The information extraction modules 210 of client 200 is characteristic information extraction from the raw information of record, and the characteristic information that extracts can be used for determining the affiliated customer group classification of user.Concrete, characteristic information comprises all kinds of software numbers, software set-up time, all kinds of software application frequency, all kinds of software upgrading frequency, all kinds of web page browsing durations etc., wherein, the classification of software can be online class, amusement class, game class, study class etc.
In one embodiment, information extraction modules 210 can be used for extracting the characteristic information for customer group classification under definite user from user's raw information, and the characteristic information that extracts is submitted to server 100.In another embodiment, information extraction modules 210 also can be used for direct raw information with the user and is submitted to server 100, by server 100 characteristic information extraction from user's raw information.
Matching module 120 is used for that the customer group of user's characteristic information and customer group feature database is differentiated feature and mates, and determines the customer group classification that the user is affiliated.
Concrete, matching module 120 is used for obtaining the customer group of mating most with user's characteristic information and differentiates feature, and then corresponding customer group classification is the affiliated customer group classification of user.
Recommending module 130 is used for recommending software according to the customer group classification of determining to the user.
Different customer group classifications can arrange recommends different software, and for example, for the game user class, then recommended games class software for the office user class, is then recommended office class software.
Above-mentioned software commending system, characteristic information by user that client 200 is extracted mates with the differentiation of the customer group in the customer group feature database in the server 100 feature, determine the customer group classification that the user is affiliated, then recommend the software corresponding with the customer group classification to the user.Because the interested software of different customer group classification is different, recommend software corresponding to customer group classification under it according to user's characteristic information to the user, can improve the possibility that the user accepts to recommend software, thereby improve the success ratio that software is recommended.
As shown in Figure 7, in one embodiment, creation module 110 comprises that sample is chosen module 112, preliminary classification module 114, characteristic information acquisition module 116 and feature database creates submodule 118, wherein:
Sample is chosen module 112 and is used for choosing sample of users.
Concrete, sample is chosen module 112 and is used for user data selected part user from storage as sample of users, the sample of users of choosing relates to different classifications as far as possible, such as network bar users class, User class, child user class, the elderly's user class, software development user class, game user class etc.
Preliminary classification module 114 is used for described sample of users is classified, and obtains the initial category of described sample of users.
Characteristic information acquisition module 116 is used for obtaining the characteristic information of sample of users.
The user data of storage comprises user's raw information in the server 100, therefore needs to extract from raw information user's characteristic information.
Preferably, characteristic information acquisition module 116 is used for obtaining the user profile of sample of users, extracts the characteristic information relevant with software in the user profile.Concrete, characteristic information acquisition module 116 can be used for by traditional Euclidean distance tolerance, by the probability metrics criterion, with the divergence criterion function, combines to extract the characteristic information relevant with software based in the methods such as differentiation entropy minimization one or more.Browse duration etc. such as what the characteristic information relevant with software that extracts comprised the upgrading frequency of the frequency of utilization of the set-up time of the number of all kinds of softwares of installation, all kinds of softwares, all kinds of softwares, all kinds of softwares and all kinds of webpages.
Feature database creates submodule 118 and is used for creating the customer group feature database according to initial category and user's characteristic information.
As shown in Figure 8, in one embodiment, feature database creates submodule 118 and comprises cluster analysis module 1180, customer group classification acquisition module 1182 and customer group feature database 1184, wherein:
Cluster analysis module 1180 is used for according to initial category user's characteristic information being carried out cluster analysis.
Concrete, cluster analysis module 1180 can be used for adopting traditional C mean algorithm, ISODATA algorithm, combines the characteristic information to the user to carry out cluster analysis based on any one or a few method in the methods such as the similarity measurement algorithm of sample and nuclear, neighbour's Function Criterion algorithm.
Customer group classification acquisition module 1182 is used for obtaining the customer group differentiation feature of customer group classification, correspondence and the reference value that described customer group is differentiated feature according to cluster analysis result.
If for the some features in the characteristic information, the corresponding eigenwert of all sample of users of a certain initial category is all more approaching, and the corresponding eigenwert with the sample of users of other initial category differs greatly, illustrate that this feature not only has cohesion to this customer group, can also this customer group and other customer group be separated by this feature, then this is characterized as the differentiation feature of this customer group.Opposite, if for the sample of users of some initial category, there is very big-difference in the eigenwert of its some feature, and then other differentiates feature to this feature as this initial user realm.For example in 100 features of game user class, there is very large difference in the eigenwert of this feature of installation number of tool-class software, and then this feature of installation number of tool-class software is not as the differentiation feature of game user class.
Further, customer group classification acquisition module 1182 can be used for determining according to the corresponding eigenwert of all sample of users of this initial category the reference value of this differentiation feature.Concrete, customer group classification acquisition module 1182 can be used for getting with this differentiations feature accordingly the mean value of all eigenwerts or the mode in all eigenwerts as the reference value of this differentiation feature of this customer group.
Further, customer group classification acquisition module 1182 can be used for determining final customer group classification, with inappropriate preliminary classification deletion or merging.If for each feature in the characteristic information, all there is very large difference between the sample of users eigenwert of a certain initial category, illustrate that the division of this initial category is nonsensical, then with this initial category deletion; If the differentiation feature of certain two initial category is roughly the same, and each differentiation feature characteristic of correspondence value is also roughly the same, and then wherein any one initial category merges in another initial category.Thereby determine the final customer group classification of customer group feature database.
Customer group feature database 1184 is used for the customer group differentiation feature of storage customer group classification, correspondence and the reference value that described customer group is differentiated feature.
As shown in Figure 9, in one embodiment, matching module 120 comprises eigenwert acquisition module 122, distance calculation module 124 and class of subscriber determination module 126, wherein:
Eigenwert acquisition module 122 is for the eigenwert of the characteristic information that obtains described user.
For example, certain user's characteristic information is: 5 sections of online class softwares, online class software application frequency 1 hour/day, 3 sections of amusement class softwares, amusement class software application frequency 2 hours/day, 2 sections of tool-class softwares, tool-class software application frequency 0.5 hour/day, but the eigenwert 5,1,3,2,2,0.5 in the characteristic information extraction then.Further, eigenwert acquisition module 122 eigenwert that can be used for extracting forms proper vector.As above in the example eigenwert is formed proper vector and be (5,1,3,2,2,0.5).
Distance calculation module 124 is used for the distance between the reference value of the not corresponding customer group differentiation feature of computation of characteristic values and all types of user realm.
In one embodiment, distance calculation module 124 is used for calculating the Euclidean distance between user's eigenwert and the reference value that all types of user group differentiates feature.Concrete, distance calculation module 124 can be used for the reference value composition characteristic vector that the eigenwert respectively eigenwert acquisition module 122 obtained and customer group are differentiated feature, and calculates two Euclidean distances between the proper vector.
In addition, when the dimension of user characteristics value vector during greater than the dimension of the reference vectors of the differentiation feature of some customer group classifications, then take the reference vectors of differentiating feature as benchmark, reject the differentiation feature reference value redundant character value in addition in the user characteristics value vector, so that user characteristics value vector is consistent with differentiation feature reference vectors dimension, calculate again Euclidean distance between the two.
Class of subscriber determination module 126 is used for obtaining minimum customer group classification corresponding to described distance, is the customer group classification under the described user.
The distance of the reference value of user's eigenwert and customer group differentiation feature is less, illustrate that then this user customer group classification corresponding with this differentiation feature is more approaching, class of subscriber acquisition module 126 is used for obtaining both customer group classifications corresponding apart from the customer group differentiation feature of minimum and is the customer group classification under the user.
In another embodiment, server 100 also comprises the module (not shown) is set, and is used for arranging the software recommendation list corresponding with the customer group classification.
Concrete, module is set can be used for according to the differentiation feature of each customer group classification corresponding software recommendation list being set, and in server 100, store the mapping table of customer group classification and software recommendation list.For example, for the game class user, the software recommendation list can be set comprise: QQ driving, hero kill, QQ three states etc.The software recommendation list that so arranges more can satisfy user's potential demand, improves the probability that the user accepts to recommend software, thereby improves the success ratio that software is recommended.
As shown in figure 10, in one embodiment, recommending module 130 comprises tabulating chooses module 132, screening module 134, wherein:
Tabulation is chosen module 132 and is used for choosing corresponding software recommendation list according to the customer group classification of determining.
Concrete, tabulation selects module 132 to be used for according to the customer group classification under the user, finds corresponding software recommendation list in the mapping table of customer group classification and software recommendation list.
It is uninstalled or recommend the user than the software of the software version update of user installation that screening module 134 is used for filtering out the user from the software recommendation list.
Concrete, screening module 134 is used for can obtaining the current software matrix of having installed of user according to user's characteristic information, software matrix and the software recommendation list of having installed are compared, can obtain the uninstalled software of user in the software recommendation list and than the software of the mounted software version update of user, then more recommend the user with the uninstalled software of user in the software recommendation list that obtains and than the software of the mounted software version update of user.In addition, client 200 can show that the user can select corresponding software to download and install after receiving the software of server 100 recommendations.
The above embodiment has only expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to claim of the present invention.Should be pointed out that for the person of ordinary skill of the art without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.
Claims (10)
1. software recommendation method may further comprise the steps:
Server creates the customer group feature database, has stored customer group classification and corresponding customer group differentiation feature in the described customer group feature database;
Client is extracted user's characteristic information, and described user's characteristic information is submitted to described server;
Described server mates described user's characteristic information and the differentiation of the customer group in described customer group feature database feature, determines the customer group classification that described user is affiliated;
Described server is recommended software according to described definite customer group classification to described user.
2. software recommendation method according to claim 1 is characterized in that, the step that described server creates the customer group feature database comprises:
Choose sample of users;
Described sample of users is classified, obtain the initial category of described sample of users;
Obtain the characteristic information of described sample of users;
Characteristic information according to described initial category and user creates the customer group feature database.
3. software recommendation method according to claim 2 is characterized in that, the described step of obtaining the characteristic information of sample of users is:
Obtain the user profile of sample of users, extract characteristic information relevant with software in the described user profile;
The step that described characteristic information according to described initial category and user creates the customer group feature database is:
According to described initial category described user's characteristic information is carried out cluster analysis;
Obtain the customer group differentiation feature of customer group classification, correspondence and the reference value that described customer group is differentiated feature according to cluster analysis result;
The reference value of described customer group classification, corresponding customer group being differentiated feature and described customer group differentiation feature is stored in the customer group feature database.
4. software recommendation method according to claim 3 is characterized in that, described server mates described user's characteristic information and the differentiation of the customer group in described customer group feature database feature, determines that the step of the customer group classification that described user is affiliated is:
Obtain the eigenwert in described user's the characteristic information;
Calculate the not corresponding described customer group of described eigenwert and all types of user realm and differentiate distance between the reference value of feature;
Obtain minimum customer group classification corresponding to described distance, be the customer group classification under the described user.
5. software recommendation method according to claim 1 is characterized in that, described method also comprises:
The software recommendation list that described Servers installed is corresponding with described customer group classification;
Described server recommends the step of software to be according to described definite customer group classification to described user:
Choose corresponding software recommendation list according to described definite customer group classification;
It is uninstalled or recommend described user than the software of the software version update of user installation to filter out the user from described software recommendation list.
6. a software commending system is characterized in that, comprises server and the client of carrying out data interaction with described server, and described server comprises:
Creation module is used for creating the customer group feature database, has stored customer group classification and corresponding customer group differentiation feature in the described customer group feature database;
Described client comprises: information extraction modules is used for extracting user's characteristic information, and described user's characteristic information is submitted to described server;
Described server also comprises:
Matching module is used for described user's characteristic information and the customer group differentiation feature of described customer group feature database are mated, and determines the customer group classification that described user is affiliated;
Recommending module is used for recommending software according to described definite customer group classification to described user.
7. software commending system according to claim 6 is characterized in that, described creation module comprises:
Sample is chosen module, is used for choosing sample of users;
The preliminary classification module is used for described sample of users is classified, and obtains the initial category of described sample of users;
The characteristic information acquisition module is for the characteristic information that obtains described sample of users;
Feature database creates submodule, is used for creating the customer group feature database according to described initial category and user's characteristic information.
8. software commending system according to claim 7 is characterized in that, described characteristic information acquisition module also is used for obtaining the user profile of described sample of users, extracts characteristic information relevant with software in the described user profile;
Described feature database creates submodule and comprises:
The cluster analysis module is used for according to described initial category described user's characteristic information being carried out cluster analysis;
Customer group classification acquisition module is used for obtaining the customer group differentiation feature of customer group classification, correspondence and the reference value that described customer group is differentiated feature according to cluster analysis result;
The customer group feature database is used for storing the customer group differentiation feature of described customer group classification, correspondence and the reference value that described customer group is differentiated feature.
9. software commending system according to claim 8 is characterized in that, described matching module comprises:
The eigenwert acquisition module is for the eigenwert of the characteristic information that obtains described user;
Distance calculation module is for the distance between the reference value of calculating described eigenwert and all kinds of described customer group differentiation features;
The class of subscriber determination module is used for obtaining the minimum customer group classification of described distance, is the customer group classification under the described user.
10. software commending system according to claim 6 is characterized in that, described server also comprises:
Module is set, is used for arranging the software recommendation list corresponding with described customer group classification;
Described recommending module comprises:
Module is chosen in tabulation, is used for choosing corresponding software recommendation list according to described definite customer group classification;
The screening module, it is uninstalled or recommend described user than the software of the software version update of user installation to be used for filtering out described user from described software recommendation list.
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