CN103455559A - Method and device for automatically recommending application - Google Patents
Method and device for automatically recommending application Download PDFInfo
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Abstract
The invention provides a method and a device for automatically recommending application. The method includes receiving an application acquisition request which is submitted by a user from a client side and comprises user identification; extracting existing user behavior information of the corresponding user from a user feature library according to the user identification; determining application categories to be recommended to the user according to the user behavior information; extracting matched application from application data sets of the application categories according to prior recommended application operation information of the user; generating corresponding application folders according to the application categories and placing the matched application into the corresponding application folders to recommend the matched application. The user behavior information comprises the prior recommended application operation information of the user. The method and the device have the advantages that individual requirements of the user can be met, the recommendation efficiency can be improved, and a recommendation coverage rate can be increased.
Description
Patented claim of the present invention be that Dec 27, application number in 2011 are 201110444798.5 the applying date, name is called the dividing an application of Chinese invention patent application of " a kind of method and device of applying automatic recommendation ".
Technical field
The application relates to technical field of information processing, particularly relates to a kind of method and a kind of device of applying automatic recommendation of applying automatic recommendation.
Background technology
Internet is an important channel of people's obtaining information, when the principal feature of conventional internet is that the user finds own interested things, need to carry out a large amount of search by browser, need artificially to filter out a large amount of incoherent results simultaneously, complex operation, and expend time in and energy.
Develop rapidly along with Internet technology, people are also more and more extensive to the demand of diverse network application (Application), but the increase along with demand, the terminal applies that people install in terminal clientsaconnect is also more and more, the various deployment that are applied in client are more and more too fat to move huge, this not only causes the waste to terminal resource, nor is convenient to management.Even adopt client-server architecture to dispose management, server end also lacks the managerial ability to follow-up use after the deployment that completes client.
Although the concept of so-called " thin-client (Thin Client) " occurred now, thin-client is sent to server process by inputs such as its mouse, keyboards, and server is back to client to result again and shows.But this tupe is limited by network transfer speeds, and the restrictions such as processing power of server, therefore, be more to be applied in the commercial LAN (Local Area Network) of enterprise-level, also be not suitable for the amusement demand of domestic consumer at present.
For making the user obtain better experience, the scheme that provides interested application automatically to recommend for the user has been provided prior art, by knowing user's interest place, initiatively for its recommendation, its interested application is provided.Yet, the mode that this application is recommended, main all by the manual recommendation of editorial staff, mainly there is following defect in the manual mode of recommending of this editorial staff:
1, efficiency is too low, too low for the recommendation coverage rate of application, for example, for application hundreds thousand of on platform, adopts artificial the recommendation every day, also can only recommend hundreds of.If want to recommend whole application in fact can't realize, and coverage rate is too low, because proportion is too low.
2, the unified principle of recommending that this recommendation is based on the editorial staff is fully carried out, the same for each user, can't meet the demand of user individual.Because the application of some recommendation is suitable for the certain user, and does not like for the certain user.
Therefore, need at present the urgent technical matters solved of those skilled in the art to be exactly: to propose a kind of mechanism of applying automatic recommendation, to meet user's individual demand, and improve and recommend efficiency and coverage rate.
Summary of the invention
The application's technical matters to be solved is to provide a kind of method of applying automatic recommendation, in order to meet user's individual demand, and improves and recommends efficiency and coverage rate.
The application also provides a kind of device of applying automatic recommendation, in order to guarantee said method application and realization in practice.
In order to address the above problem, the embodiment of the present application discloses a kind of method of applying automatic recommendation, comprising:
Receive the user and obtain request from the application of client submission, the described application request of obtaining comprises user ID;
Extract the existing user behavior information of relative users from the user characteristics storehouse according to described user ID;
Determine the applicating category of recommending to the user according to described user behavior information;
Extract the application of coupling is recommended in the application data sets of described applicating category.
Preferably, described method can also comprise:
Gather the user behavior information of submitting to after request is obtained in described application, write in the user characteristics storehouse by user ID.
Preferably, described user behavior information also comprises user's local operation behavioural information, and/or, user's online operation behavior information;
Describedly according to user behavior information, determine that the step of the applicating category of recommending to the user can comprise:
From described user's local operation behavioural information and/or online operation behavior information, extract tag along sort and the first corresponding operation frequency;
Described tag along sort is converted to corresponding applicating category by default correlation rule; The transformation rule that described default correlation rule is tag along sort and applicating category;
Operation information from described user for exemplary application formerly, extract user operated application message and second corresponding operation frequency in the Preset Time section, described application message comprises applicating category;
Calculate the weight of each applicating category according to the described first operation frequency and the second operation frequency, sorted from high to low by the weight of described applicating category;
Front n the applicating category that extracts predetermined number is the applicating category of recommending to the user; Wherein, described n is greater than 1 positive integer.
Preferably, can generate the application data set of certain applicating category by following steps:
Obtain the application of same applicating category, described application has tag along sort;
Determine main application and application to be recommended in described application, and calculate the similarity of application to be recommended and main application according to the tag along sort of each application;
Obtain the quality score parameter of described application to be recommended;
Extract respectively the corresponding application to be recommended of same main application, sorted from high to low by similarity and the quality score parameter of each application to be recommended, and extract before predetermined number the application to be recommended of m; Wherein, described m is greater than 1 positive integer;
The application data set that main application and the application to be recommended of the correspondence extracted is formed to the current application classification.
Preferably, the described application data sets at applicating category can comprise for the step of the formerly application of the operation information extraction coupling of exemplary application according to the user:
For the operation information of exemplary application formerly, add up main application and the 3rd corresponding operation frequency according to the user, described master is applied as the operated application of user;
Application data sets at corresponding applicating category, application to be recommended according to described main application fetches coupling, and in the application to be recommended of described coupling, the application to be recommended that described the 3rd operation frequency is extracted respectively to some as the weight of application fetches, extract the application to be recommended that meets the first predetermined number altogether.
Preferably, the described application data sets at applicating category can also comprise for the step of the formerly application of the operation information extraction coupling of exemplary application according to the user:
Obtain applicating category corresponding to main application, in same applicating category, by described the 3rd operation frequency, described main application is sorted, extract front k main application of predetermined number; Wherein, described k is greater than 1 positive integer;
Extracted main application is matched in twos, calculate the described total degree that the main application of pairing occurs simultaneously in twos, generate frequent 2 collection;
Calculate the number of times that each main application occurs separately, generate frequent 1 collection;
Calculate the degree of confidence of each main application according to described frequent 2 collection and frequent 1 collection, and by degree of confidence, main application is sorted;
By the extracted application to be recommended that meets the first predetermined number, and the described main application by the degree of confidence sequence is mated, and generates the final coupling application of recommending.
The embodiment of the present application discloses a kind of device of applying automatic recommendation simultaneously, comprising:
The request receiving module, obtain request for receiving the user from the application of client submission, and the described application request of obtaining comprises user ID;
Behavioural information extraction module formerly, for extracting the existing user behavior information of relative users from the user characteristics storehouse according to described user ID;
The applicating category determination module, for determining the applicating category of recommending to the user according to described user behavior information;
Coupling application acquisition module, the application of extracting coupling for the application data sets at described applicating category;
The application recommending module, recommended for the application by described coupling.
Preferably, described device also comprises:
The behavioral statistics module, for gathering the user behavior information of submitting to after request is obtained in described application, write in the user characteristics storehouse by user ID.
Preferably, described user behavior information also comprises user's local operation behavioural information, and/or, user's online operation behavior information;
Described applicating category determination module can comprise:
First Characteristic extracts submodule, for the local operation behavioural information from described user and/or online operation behavior information, extracts tag along sort and the first corresponding operation frequency;
The conversion submodule, for being converted to corresponding applicating category by described tag along sort by default correlation rule; The transformation rule that described default correlation rule is tag along sort and applicating category;
Second Characteristic extracts submodule, for from described user for the operation information of exemplary application formerly, extract user operated application message and second corresponding operation frequency in the Preset Time section, described application message comprises applicating category;
The sequence submodule, for according to the described first operation frequency and the second operation frequency, calculating the weight of each applicating category, sorted from high to low by the weight of described applicating category;
Classification is selected submodule, for the applicating category of front n applicating category for recommending to the user that extracts predetermined number; Wherein, described n is greater than 1 positive integer.
Preferably, described device can also comprise:
The application data set generation module, for generating the application data set of each applicating category: specifically comprise:
Submodule is obtained in similar application, and for obtaining the application of same applicating category, described application has tag along sort;
The similarity calculating sub module, in described application, determining main application and application to be recommended, and calculate the similarity of application to be recommended and main application according to the tag along sort of each application;
Quality score parameter acquiring submodule, for obtaining the quality score parameter of described application to be recommended;
Application fetches submodule to be recommended, for extracting respectively the corresponding application to be recommended of same main application, sorted from high to low by similarity and the quality score parameter of each application to be recommended, and extract before predetermined number the application to be recommended of m; Wherein, described m is greater than 1 positive integer;
Application data set forms submodule, for main application and the correspondence application to be recommended of extracting being formed to the application data set of current application classification.
Preferably, described coupling application acquisition module can comprise:
Main applied statistics submodule, for according to the user for the operation information of exemplary application formerly, add up main application and the 3rd corresponding operation frequency, described master is applied as the operated application of user;
Submodule is determined in application to be recommended, for the application data sets at corresponding applicating category, application to be recommended according to described main application fetches coupling, and in the application to be recommended of described coupling, the application to be recommended that described the 3rd operation frequency is extracted respectively to some as the weight of application fetches, extract the application to be recommended that meets the first predetermined number altogether.
Preferably, described coupling application acquisition module can also comprise:
Submodule is chosen in main application, for obtaining applicating category corresponding to main application, in same applicating category, by described the 3rd operation frequency, described main application is sorted, and extracts front k main application of predetermined number; Wherein, described k is greater than 1 positive integer;
Frequent 2 collection calculating sub module, match in twos for the main application by extracted, and calculates the described total degree that the main application of pairing occurs simultaneously in twos, generates frequent 2 collection;
Frequent 1 collection calculating sub module, the number of times occurred separately for calculating each main application, generate frequent 1 collection;
The confidence calculations submodule, for according to described frequent 2 collection and frequent 1 collection, calculating the degree of confidence of each main application, and sorted to main application by degree of confidence;
Submodule is determined in the coupling application, and for the application to be recommended that meets the first predetermined number by extracted, and the described main application by the degree of confidence sequence is mated, and generates the final coupling application of recommending.
Compared with prior art, the application has the following advantages:
The application of the application based on having recommended to the user, analysis user is for the operation information of described formerly exemplary application, online operation behavior information and/or local operation behavioural information in conjunction with the user, determine the applicating category of user behavior information institute preference, then in the application data sets of corresponding applicating category, operation information according to above-mentioned user for described formerly exemplary application, online operation behavior information and/or local operation behavioural information in conjunction with the user, extract the application that meets user interest most, these application being put into to the file of corresponding applicating category is recommended, thereby set up contact between application and user, fully met user's individual demand, and recommendation efficiency and the coverage rate of application have effectively been improved.
Moreover the application is usingd user interface as entrance, directly on interface or by the link on interface by the application file clip icon to user's exemplary application obtain required application so that the user is faster easier, be convenient for users to operate; And, can point out the user use to this application by icon as the mode of application entrance, but before the real choice for use of user, not actual installation this apply corresponding configuration file, like this, can before use and exceed and take client resource.In addition, the icon in user interface can be concentrated and be disposed or push by the network side central server, and this has just prevented that rogue program from arbitrarily adding the malice icon in interface, further improved security.
The accompanying drawing explanation
Fig. 1 is a kind of flow chart of steps of applying the embodiment of the method for automatic recommendation of the application;
Fig. 2 is a kind of structured flowchart of applying the device embodiment of automatic recommendation of the application.
Embodiment
For above-mentioned purpose, the feature and advantage that make the application can become apparent more, below in conjunction with the drawings and specific embodiments, the application is described in further detail.
The core idea of the embodiment of the present application is, application based on having recommended to the user, analysis user is for the operation information of described formerly exemplary application, online operation behavior information and/or local operation behavioural information in conjunction with the user, determine the applicating category of user behavior information institute preference, then in the application data sets of corresponding applicating category, operation information according to above-mentioned user for described formerly exemplary application, online operation behavior information and/or local operation behavioural information in conjunction with the user, extract the application that meets user interest most, these application being put into to the file of corresponding applicating category is recommended, thereby set up contact between application and user.
With reference to Fig. 1, a kind of flow chart of steps of applying the embodiment of the method for automatic recommendation that it shows the application, specifically can comprise the steps:
Request is obtained in the application that step 101, reception user submit to from client, and the described application request of obtaining comprises user ID;
In specific implementation, the user starts client can trigger the request of obtaining of applying, and the user also can the manual triggers application obtain request, and the application is not restricted this.
Described user characteristics can record following information: user ID Mid in storehouse, the tag along sort tag of user behavior information, and, corresponding operation frequency weight.
In a preferred embodiment of the present application, described user's behavioural information can comprise user's local operation behavioural information, and/or, user's online operation behavior information, and the user is for the operation information of exemplary application formerly.Described user's local operation behavioural information and online operation behavior information usually can be with tag along sort (tag) information, for example, the video of opening at local operation for the user, with classification label informations such as the fiery shadow person of bearing, animation, serial, illusion, risk, bank Ben Qishi; Or as, the network address accessed on the net for the user, with the classification label informations such as king of video, film, comedy movie, comedy.Described application also has the information of applicating category and tag along sort.
Described user's local operation behavioural information and online operation behavior information can be gathered by the client software be arranged on subscriber equipment, wherein, described subscriber equipment can comprise all kinds of intelligent terminals such as computing machine, notebook computer, mobile phone, PDA, panel computer.Several collection users' local operation behavioural information below is provided, and/or, the example of user's online operation behavior information:
Example 1, gather the online operation behavior information of user in a period of time by browser, comprises the network address of access and corresponding access times etc.;
As the online operation behavior information gathered by browser in user 15 days is:
| The access network address | Access times |
| 4939.com | 31 |
| Qiyi.com | 2 |
| Youku.com | 7 |
| 7k7k.com | 4 |
Example 2, by being arranged on the local operation behavioural information of the fail-safe software collection user on subscriber equipment, as online operation behavior information and the local behavioural information gathered by 360 net shields in user 15 days is: open MPC and number of times thereof, open certain game and number of times thereof etc.
Certainly, the method for above-mentioned collection and the information of collection are all only as example, and it is all feasible that those skilled in the art adopt any mode to gather required user behavior information according to actual conditions, the embodiment of the present application to this without being limited.
In a preferred embodiment of the present application, described step 103 specifically can comprise following sub-step:
Sub-step S11, from described user's local operation behavioural information and/or online operation behavior information, extract tag along sort and the first corresponding operation frequency;
Sub-step S12, described tag along sort is converted to corresponding applicating category by default correlation rule; The transformation rule that described default correlation rule is tag along sort and applicating category;
Sub-step S13, the operation information from described user for exemplary application formerly, extract user operated application message and second corresponding operation frequency in the Preset Time section, described application message comprises applicating category;
Sub-step S14, according to the described first operation frequency and the second operation frequency, calculate the weight of each applicating category, sorted from high to low by the weight of described applicating category;
Front n applicating category of sub-step S15, extraction predetermined number is the applicating category of recommending to the user; Wherein, described n is greater than 1 positive integer.
In practice, can, according to by the technician, setting in advance applicating category, by the analysis user behavioural information, obtain the applicating category of user behavior information conforms.For example, the application file folder basic classification set in advance has 20, and by the analysis user behavioural information, finding that there is some basic classification is unwanted for the active user, can to divide applicating category that user behavior information belongs to be behavioural habits before more being close to the users 3 or 5.For example, video, game, education etc.
For making those skilled in the art understand better the application, below by a concrete example explanation, determine the process of the applicating category of recommending to the user according to user behavior information:
Data Source:
(1) the net shield data of nearest 15 days: Data11;
(2) user of nearest 15 days uses application interpolation or the click logs of safety desktop: Data12;
The data layout of Data11 is:
User ID Mid tag along sort Interest the first operation frequency weight1
The data instance of Data11 is as follows:
0000175873530b93d848614a0c188c5b novel-dm 1；
000020218613d5fc8e05c314dba32956 comic-dm 4；
00002e3bb9037870973b328078971c98 4399-dm 1；
The data layout of Data12 is:
Original log
The data instance of Data12 is as follows:
123.97.168.210 - - [15/Oct/2011:23:00:01 +0800] ]GET /stat.html type=open&action=yingyongdianji&fangshi=2&Appid=102000032&fenleiid=4&from=0&leixing=2&style=fullscreen&uid=1&pid=softmgr&m=45c06dc58f5ccb64c162b646fcecc541&modulever=1.4.0.1103&appver=1.4.0.1103HTTP/1.1″200 0″-″″Mozilla/4.0(compatible;MSIE7.0;Windows NT6.1;Trident/4.0;GTB7.1;SLCC2;.NET CLR2.0.50727;.NET CLR3.5.30729;.NET CLR3.0.30729;Media Center PC6.0)″
Step1: will from net shield data Data11, extract tag along sort Interest, be converted to the basic classification of the user interest under application file folder taxonomic hierarchies by default transformation rule table (yunCatToZhuoMianCat.conf), be about to described class label and be converted to corresponding applicating category.In described default transformation rule table yunCatToZhuoMianCat.conf form, can comprise: the information of tag along sort, applicating category title AppName and applicating category sign Appid, as shown in the table:
| Interest | AppName | Appid |
| 4399-dm | Game | 5 |
| comic-dm | The fashion amusement | 8 |
| novel-dm | Novel | 11 |
The result of Data11 conversion is as shown in the table:
Step2: by resolving original log Data12, calculate the operation frequency (the second operation frequency) that each Mid clicked or added each application in nearest 15 days, and, according to the Appid_name classification table of comparisons, determine the corresponding applicating category of user interest.
Wherein, Appid_name classification table of comparisons form is as follows:
Application identities Appid applicating category sign fenleiid tag along sort tag Apply Names AppName.
The data instance of the Appid_name classification table of comparisons is as follows:
If by resolving original log Data12, the data of calculating the operation frequency (second operates frequency Weight2) that each Mid clicked or added each application in nearest 15 days are as shown in the table:
| Mid | Appid | Weight2 |
| 00008fc5c27c3354e1e0c9b6b7527dd9 | 100026002 | 1 |
| 0000b5d11c0c8ea46817fc32f467c3ba | 100013330 | 3 |
| 0001555e4ea2b299b6fbc55f46eeb771 | 100114314 | 4 |
Contrast the above-mentioned Appid_name classification table of comparisons, determine that the corresponding applicating category of user interest is as shown in the table:
Step3: the result of Step1 and Step2 is weighted on average according to the first operation frequency and the second operation frequency, then according to final score, sorted, get the applicating category of top9 for recommending to the user, i.e. the applicating category of the final classification application file of showing.
For example: for some Mid, the result of Step1 is: type1 clicks n1 time, and type2 clicks n2 time, and type3 clicks n3 time
The step2 result is: type1 behavior N1 time, and type2 clicks N2 time, and type3 clicks N3 time
Score1=n1*0.6+N1*0.4, score2=n2*0.6+N2*0.4, score3=n3*0.6+N3*0.4
Sorted by score, got the applicating category of applicating category for recommending to the active user of first 9.
In specific implementation, if being analyzed to divided applicating category, user behavior information can't reach specified quantity, if in employing, example can only generate three classifications, can't meet the demand of 9 applicating categories, the applicating category that the actual access times of the network user that can add up according to high in the clouds are maximum or the most newly-installed applicating category carry out polishing as the applicating category of recommending.
Certainly, the method of above-mentioned division user behavior information institute belonging kinds is only as example, it is all feasible that those skilled in the art adopt a kind of mode according to actual conditions, for example, do not extract the Main classification label, directly by user behavior information with label be converted to applicating category according to presetting rule; Perhaps, directly extract tag along sort as applicating category etc., the application is not restricted this.
Described application (Application) refers to the various services that the user uses on network, as application program, webpage, video, novel, music, game, news, shopping and mailbox etc.Application data set comprises a plurality of application, derives from each open platform.In the embodiment of the present application, application can be with classification information (applicating category) and some tag along sorts.
In a kind of preferred embodiment of application, can generate by following sub-step the application data set of certain applicating category:
Sub-step S21, the application of obtaining same applicating category, described application has tag along sort;
Sub-step S22, definite main application and application to be recommended in described application, and calculate the similarity of application to be recommended and main application according to the tag along sort of each application;
Sub-step S23, obtain the quality score parameter of described application to be recommended;
Sub-step S24, extract the corresponding application to be recommended of same main application respectively, sorted from high to low by similarity and the quality score parameter of each application to be recommended, and extract before predetermined number the application to be recommended of m; Wherein, described m is greater than 1 positive integer;
Sub-step S25, main application and the application to be recommended of the correspondence extracted are formed to the application data set of current application classification.
Above preferred embodiment for the application of same applicating category, according to the similarity between its tag along sort computing application, forms an application data set that comprises main application and application to be recommended.
For making those skilled in the art understand better the application, below by a concrete example, the process of above-mentioned generation application data set is described.
1). according to the applicating category of application and the similarity between the tag along sort tag computing application app of application:
The data layout of input data is: Appid fenleiID tag1 tag2 tag3 tag4 tag5 tag6 tag7 tag8
The data layout of destination file Data1 is: main application identities (main Appid) application identities to be recommended (Appid to be recommended) similarity Similarity
Similarity calculating method is:
In identical applicating category, a former i tag is as the class mark, and in identical mark, app carries out combination of two, calculates its similarity, and computing formula is: Similarity=i/ (n1+n2-i); Wherein, n1 is app1(application 1) number of back tag, n2 is app2(application 2) number of back tag; The i minimum is 2, is n1 to the maximum, carries out searching loop.For example:
The input data are:
100030071 4 other 2010 continents of film story of a play or opera comedy love monarch Wu Chen Liu Yanjun Xie Xiaoming
Other 2009 Korea S of the shining Shen Tailuo of 100030073 4 film story of a play or opera comedy action Jin Henajiang will
Other 2009 U.S. of the triumphant strange money De Lekanteburui Alex Pu Luoyasi of the terrible Nicholas of 100030074 4 film suspense science fiction
......
Destination file Data1 is:
100030071 100030073 0.25
100030071 100030074 0.11
100030073 100030074 0.11
......
2). in same main Appid, to Appid to be recommended, by the quality score of similarity and Appid (every daily downloads, user's scoring), undertaken integrated ordered, be Appid similarity * similarity weight+Appid quality score * (1-similarity weight), the highest front 50 Appid to be recommended of intercepting integrate score, then merge into a line;
Input data: the destination file of Data1(previous step)
The form of output data Data2 is as follows: main Appid Appid1 to be recommended Appid2 to be recommended Appid3 to be recommended Appid4 to be recommended
For example:
Input data Data1 is:
Output data Data2 is:
100030071 100030073 100030074……
100030073 100030071 100030074……
100030074 100030071 100030073……
In a kind of preferred embodiment of application, described step 104 may further include following sub-step:
Sub-step S31, according to the user for the operation information of exemplary application formerly, add up main application and the 3rd corresponding operation frequency, described master is applied as the operated application of user;
Sub-step S32, in the application data sets of corresponding applicating category, application to be recommended according to described main application fetches coupling, and in the application to be recommended of described coupling, the application to be recommended that described the 3rd operation frequency is extracted respectively to some as the weight of application fetches, extract the application to be recommended that meets the first predetermined number altogether.
For making those skilled in the art understand better the application, below by a concrete example, above-mentioned sub-step S31-S32 is described.
Step1：
3). according to the application operating user behaviors log of Mid, statistics Mid adds or clicks the number of times of each app;
Input data: Mid adds or clicks the log recording (the application operating user behaviors logs of nearest 30 days) of application;
The form of output data Data3 is: the id of the app of Mid master Appid(click or interpolation)
Weight3(the 3rd operation frequency)
If the input data are:
27.185.166.230--[20/Aug/2011:10:11:47+0800]″GET/stat.html type=open&action=yingyongdianji&fangshi=2&Appid=103352&fenleiid=10001&from=0&leixing=1&style=fullscreen&uid=1&pid=h_home_inst&m=71ddd8f9f1c84e16438ef109f4b6d77b&modulever=1.4.0.1041&appver=1.4.0.1041HTTP/1.1″200 0″-″″Mozilla/4.0(compatible;MSIE7.0;Windows NT6.0;SLCC1;.NET CLR 2.0.50727;Media Center PC5.0;.NET CLR3.5.30729;.NET CLR3.0.30618)″
111.127.218.150--[20/Aug/2011:10:11:47+0800]″GET/stat.html type=open&action=tianjiayingyong&Appid=100018815&fenleiid=4&sort=%b6%af%bb%ad&from=5&style=fullscreen&uid=1&pid=h_home_inst&m=9e236bafe13c8348247781c2d0fab7a7&modulever=1.0.2.1025&appver=1.4.0.1040HTTP/1.1″200 0″-″″Mozilla/4.0(compatible;MSIE6.0;Windows NT5.1;SV1;4399Box.909)″
58.50.201.130--[20/Aug/2011:10:11:47+0800]″GET/stat.html type=open&action=yingyongdianji&fangshi=2&Appid=103352&fenleiid=10001&from=6&leixing=1&style=iphone&uid=1&pid=h_home&m=3d3e77348ff2fbfa6af7c3751a00edae&modulever=1.4.0.1040&appver=1.4.0.1040HTTP/1.1″2000″-″″Mozilla/4.0(compatible;MSIE6.0;Windows NT5.1)″
110.178.40.7--[20/Aug/2011:10:11:47+0800]″GET/stat.html type=open&action=yingyongdianji&fangshi=2&Appid=100000525&fenleiid=4&from=0&leixing=2&style=fullscreen&uid=1&pid=softmgr&m=f16b5a2c01d64fcfa3ff5f035ce74677&modulever=1.4.0.1041&appver=1.4.0.1041HTTP/1.1″2000″-″″Mozilla/4.0(compatible;MSIE7.0;Windows NT5.1;Trident/4.0)″
58.50.201.130--[20/Aug/2011:10:11:47+0800]″GET/stat.html type=open&action=zuixiaohuazhuomian&count=90468&uid=1&pid=h_home&m=3d3e77348ff2fbfa6af7c3751a00edae&modulever=1.4.0.1040&appver=1.4.0.1040HTTP/1.1″2000″-″″Mozilla/4.0(compatible;MSIE6.0;Windows NT5.1)″
......
Output data Data3 is:
4). according to Data2 and Data3, by main app, mated, then sorted according to weight
Mid1 fenleiid1 Appid1 Appid2 Appid3 Appid4 …… weight
Mid1 fenleiid2 Appid11 Appid22 Appid33 Appid44…… weight
According to the weight size, weight is larger, and intercepting app is more in such the inside, adopts the mode of random intercepting, altogether intercepts 50 Appid, then take Mid and fenleiid as key word is merged, and generates destination file and is
Mid1 fenleiid Appid1 Appid2 Appid3 Appid4……Appid20
For example:
Input data: Data2 and Data3
Wherein, Data2 is:
001（100 101 102 103 104 105 106 107 108 109……）
002（201 202 203 204 205 206 207 208 209 210……）
003（301 302 303 304 305 306 307 308 309 310……）
008（801 802 803 804 805 806 807 808 809 810……）
......
Suppose that the part in above-mentioned Data2 bracket is that front 50 the Appid Data3s the highest with main app similarity are:
Xx1 001 5
Xx1 002 3
Xx1 003 2
Xx1 008 5
......
By the Appid_name table of comparisons of classifying, map out its classification, obtain Data33 and be:
Xx1 1 001 5
Xx1 1 002 3
Xx1 1 003 2
Xx1 2 008 5
......
Intermediate output data:
Article 1, data: Xx1 1,100 101 102 103 104 105 106 107 108 109 ... 5
Article 2, data: Xx1 1 201 202 203 204 205 206 207 208 209 210 ... 3
Article 3, data: Xx1 1 301 302 303 304 305 306 307 308 309 310 ... 2
Article 4, data: Xx1 2,801 802 803 804 805 806 807 808 809 810 ... 5
......
According to weighing computation method:
Article 1, randomly draw 20=10 Appid of 5/ (5+3+2) * in data;
Article 2, randomly draw 20=6 Appid of 3/ (5+3+2) * in data;
Article 3, randomly draw 20=4 Appid of 2/ (5+3+2) * in data;
Final output data are:
Xx1 1 100 101 102 103 104 105 106 107 108 109 204 205 206 207 208 209 306 307 308 303
Xx2 2 801 802 803 804 805 806 807 808 809 810……
......
More preferably, described step 104 may further include following sub-step:
Sub-step S33, obtain applicating category corresponding to main application, in same applicating category, by described the 3rd operation frequency, described main application is sorted, extract front k main application of predetermined number; Wherein, described k is greater than 1 positive integer;
Sub-step S34, extracted main application is matched in twos, calculate the described total degree that the main application of pairing occurs simultaneously in twos, generate frequent 2 collection;
Sub-step S35, calculate the number of times that each main application occurs separately, generate frequent 1 collection;
Sub-step S36, according to described frequent 2 collection and frequent 1 collection, calculate the degree of confidence of each main application, and by degree of confidence, main application is sorted;
Sub-step S37, by the extracted application to be recommended that meets the first predetermined number, and the described main application by the degree of confidence sequence is mated, and generates the final coupling application of recommending.
One of core idea of the present embodiment is, based on user add or the behavior of clicking application, is that its preference to application finds similar application, then, according to user's the i.e. historical application of adding or clicking of historical preference, to it, recommends similar application.From the angle of calculating, exactly all users are carried out to the similarity between computing application to the preference of certain application as a vector, after the similar application be applied, also do not mean the application of preference according to the preference prediction active user of user's history, calculate the list of application of a sequence as recommendation.
For making those skilled in the art understand better the application, below by a concrete example, above-mentioned sub-step S33-S37 is described.
Step2：
1). add and click the user behaviors log of application according to Mid, statistics Mid adds or clicks the number of times of each app;
Input data: Mid adds or clicks and apply daily record (the application operating user behaviors logs of nearest 30 days);
The form of output data Data1 is: the app that Mid master Appid(clicks or adds)
Weight3(the 3rd operation frequency)
For example: the input data are:
27.185.166.230--[20/Aug/2011:10:11:47+0800]″GET/stat.html type=open&action=yingyongdianji&fangshi=2&Appid=103352&fenleiid=10001&from=0&leixing=1&style=fullscreen&uid=1&pid=h_home_inst&m=71ddd8f9f1c84e16438ef109f4b6d77b&modulever=1.4.0.1041&appver=1.4.0.1041HTTP/1.1″200 0″-″″Mozilla/4.0(compatible;MSIE7.0;Windows NT6.0;SLCC1;.NET CLR 2.0.50727;Media Center PC5.0;.NET CLR3.5.30729;.NET CLR3.0.30618)″
111.127.218.150--[20/Aug/2011:10:11:47+0800]″GET/stat.html type=open&action=tianjiayingyong&Appid=100018815&fenleiid=4&sort=%b6%af%bb%ad&from=5&style=fullscreen&uid=1&pid=h_home_inst&m=9e236bafe13c8348247781c2d0fab7a7&modulever=1.0.2.1025&appver=1.4.0.1040HTTP/1.1″200 0″-″″Mozilla/4.0(compatible;MSIE6.0;Windows NT5.1;SV1;4399Box.909)″
58.50.201.130--[20/Aug/2011:10:11:47+0800]″GET/stat.html type=open&action=yingyongdianji&fangshi=2&Appid=103352&fenleiid=10001&from=6&leixing=1&style=iphone&uid=1&pid=h_home&m=3d3e77348ff2fbfa6af7c3751a00edae&modulever=1.4.0.1040&appver=1.4.0.1040HTTP/1.1″200 0″-″″Mozilla/4.0(compatible;MSIE6.0;Windows NT5.1)″
110.178.40.7--[20/Aug/2011:10:11:47+0800]″GET/stat.html type=open&action=yingyongdianji&fangshi=2&Appid=100000525&fenleiid=4&from=0&leixing=2&style=fullscreen&uid=1&pid=softmgr&m=f16b5a2c01d64fcfa3ff5f035ce74677&modulever=1.4.0.1041&appver=1.4.0.1041HTTP/1.1″2000″-″″Mozilla/4.0(compatible;MSIE7.0;Windows NT5.1;Trident/4.0)″
58.50.201.130--[20/Aug/2011:10:11:47+0800]″GET/stat.html type=open&action=zuixiaohuazhuomian&count=90468&uid=1&pid=h_home&m=3d3e77348ff2fbfa6af7c3751a00edae&modulever=1.4.0.1040&appver=1.4.0.1040HTTP/1.1″2000″-″″Mozilla/4.0(compatible;MSIE6.0;Windows NT5.1)″
Output data Data1 is:
Shone upon with the Appid_name table of comparisons, find out the applicating category that each Appid is corresponding, result is as follows:
2). in same Mid and feileid, its click or the app that added, by clicking or adding number of times and sorted, are got front 20 Appid, and are included into a line.
Input data: the output data of Data1(previous step)
The data structure of output data Data2 is: Mid fenleiid Appid1 weight1 Appid2 weight2 Appid3 weight3
For example: the input data:
00082bf0489199360bce8a06693ef3f5 1 100115004 3
00083ebafe4eb71596f45dfa821f73d5 1 102028904 5
000887c0d3498c7c43cecb566a6333e4 8 102020030 3
0008ce3ae13aa6332794861b275861ad 10 102005157 2
......
Output data Data2 is:
007a2663bac10378bcaa874be36a2d97 1 102006053 2 102028976 1 100000913 1
007abe31b117a554df0fefc2a91200c2 2 120042762 11 102023358 2 100000568 2 102010364 1 100115004 1
007b3dc8a31a6627a6e2468f789aa078 8 102007826 19 102020628 6 102007664 6 102028968 4 100012183 3 100012315 3 102043563 2 102022076 2 102000032 2 102031006 2 110004672 2 102007377 2 101000009 2 110091072 2 100030320 2 102044509 2 102043791 2 102044243 2 102044665 2 100040423 2
007bb8043a487ed3a690aa6d461a3c10 10 102019572 3
007d072a6bfe28591f0eb4c5d533784c 18 100000525 31 100000289 19 101000053 16 102005903 6 100000625 4 102020030 4 102020628 3 120055003 3 100000913 3 102001686 2 100034506 2 102005985 2 100000801 2 102044292 2 110153628 2 100115575 2 100045261 2 100115650 2 100103773 2 100102029 2
3). in a pair of Mid and fenleiid, Appid matches in twos, and note is common to be occurred 1 time, then take two Appid as 1 class, calculates the total degree that two Appid occur simultaneously, generates frequent 2 collection.
Input data: the output data of Data2(previous step)
The data structure of output data Data3 is: Appid1 Appid2 weight(occurrence number)
For example: the input data
Xx1 1 001 002 003 004 ……
Xx2 2 001 002 003 ……
Xx3 15 002 004 ……
......
The intermediate result file:
Output data Data3 is:
4). calculate frequent 1 collection, note is calculated the number of times that each Appid occurs
Input data: Data1(the 1st) the output data of step)
The data structure of output data Data4 is: Appid weight(occurrence number)
For example:
The input data:
Output data Data4 is:
001 1
002 2
003 3
......
5). calculate degree of confidence, and sorted by degree of confidence
For example: the number of times that frequent 2 pooled applications A and application B occur is N, and the number of times that frequent 1 pooled applications A occurs is M, with respect to A, the degree of confidence of B is N/M, according to degree of confidence, is sorted, and gets the application of sequence front 50, and be applied as class with head, and merging to a line, structure is:
A B C D……
Input data: Data3 and Data4
The data structure of output data Data5 is: Appid Appid1 weight1 Appid2 weight2 ... Appid50 weight50
For example:
Input data Data3 is:
Input data Data4 is:
001 100
002 200
Output data Data5 is:
001 100 101 102 103 104 106……
002 201 201 101 102 ……
6). according to Data1 and Data5, according to user Appid, mated the generating recommendations result.
For example:
Data1：
Xx1 1
001 10
Xx1 2
002 5
......
Data5：
001 100 101 102 103 104 105 106 107……
002 200 201 202 203 204 205 206 207……
According to the Appid that indicates underscore, mated, generation intermediate result is:
Xx1 1 100 101 102 103 104 105 106 107…… 10
Xx1 2 200 201 202 203 204 205 206 207…… 5
In with a pair of Mid and fenleiid, according to weight, sorting, to get front 50 application as follows:
Xx1 1 100 101 102 103 104 105 106 107…… 200 201 202 203 204 205 206 207……
In specific implementation, can further the result of Step1 and Step2 in above-mentioned example be merged according to Mid and fenleiid, the applicating category definite by step 103 filtered, only get the application data of definite applicating category, then in each applicating category, get at random 50 application as recommendation results.If there is no data in certain applicating category, can adopt arbitrary mechanism to carry out polishing, for example, put into the most popular application, up-to-date application etc.
Certainly, above-mentioned searching with the method for user behavior information matches application only is used as example, it is also feasible that those skilled in the art adopt other computing method, for example, the matching degree of the tag along sort that the tag along sort by calculating user behavior information and respective classes application data sets are applied etc., the application to this without being limited.
Application the embodiment of the present application, generate the application file folder by category, under respective classes, with the application of user behavior information matches, in the application file folder of corresponding classification, to the user, recommended, thus the resource that is conducive to save subscriber equipment.
In specific implementation, the application file folder for recommending the user can be represented in the different split screens of desktop, and preferably, height and width that can also the User split screen, determine the number of the application file folder of recommending in each split screen.Application the embodiment of the present application, the order that represents of described application file folder is to arrange from high to low according to the operation frequency of the corresponding Main classification label of each applicating category, so the application file folder is to represent from high to low to the user according to the matching degree of user interest; And the application in the application file folder is also sorted by weight, is also to represent to the user from high to low according to the matching degree of user interest, thereby the more convenient user's of energy operation makes the user obtain better experience.
In specific implementation, can in the user interface of terminal desktop, unify to show and press from both sides corresponding icon with a plurality of application files, each icon represents an application file folder, by icon as the mode with applying entrance.This patterned exhibition method is very directly perceived for the user, and easy to use and management.For example, the icon of showing the application file folder in user interface comprises " video ", " novel ", " education " and " game ", click the icon of " video " application file folder the user after, enter the subwindow of this application file folder, in subwindow, show a plurality of application icons such as TV play, film, animation, variety are arranged.Can point out the user use to this application by icon as the mode of application entrance, but before the real choice for use of user, not actual installation this apply corresponding configuration file, like this, not only can be user-friendly, and before use and exceed and take client resource.
Icon in user interface can be concentrated and be disposed or push by the network side central server, and this has just prevented that rogue program from arbitrarily adding the malice icon in interface, further improved security.There is the configuration file of central server centralized management can comprise the corresponding reference address of applying, present specification, and the unfolding mode of described application, or their any combination.
For example, for web application, by central server, the mode by configuration file is sent to end side in the address of web access, and this has just prevented rogue program the distorting reference address of end side.
And, the profile information that the network side central server can upgrade by the mutual acquisition with the third party content server, for example, if the reference address of certain application changes, address information after server can upgrade by the mutual acquisition with content server, and send over by configuration file, stopped to change because of reference address the opportunity stayed to rogue program.
In addition, subscriber equipment, after the configuration file that obtains the application corresponding with described icon, can also upgrade the display state of this icon, further to point out the user.For example, before not obtaining configuration file, icon can be black and white, or dark-coloured, and, after acquisition, can become colour or light tone.
Also it should be noted that, the application file clip icon of showing in the end side user interface, can be one or more, can determine according to different displaying rules.For example, when using an icon, this icon can be used as the unified entrance of the application of a plurality of subordinates or subordinate's icon, when wherein any one application obtains lastest imformation, at this entrance icon place, all can obtain prompting.
In a preferred embodiment of the present application, can also comprise the steps:
Gather the user behavior information of submitting to after request is obtained in described application, write in the user characteristics storehouse by user ID.
By setting up the user characteristics storehouse, user behavior information can be unified in to server end or high in the clouds is processed, in such an embodiment, can in the user characteristics storehouse, work as inferior operation behavior information by recording user, and previous operation behavior information determines that the application file folder that should recommend to the user reaches application accordingly according to the user characteristics storehouse.
It should be noted that, for embodiment of the method, for simple description, therefore it all is expressed as to a series of combination of actions, but those skilled in the art should know, the application is not subject to the restriction of described sequence of movement, because according to the application, some step can adopt other orders or carry out simultaneously.Secondly, those skilled in the art also should know, the embodiment described in instructions all belongs to preferred embodiment, and related action and module might not be that the application is necessary.
With reference to Fig. 2, show a kind of structured flowchart of applying the device embodiment of automatic recommendation of the application, specifically can comprise as lower module:
Request receiving module 201, obtain request for receiving the user from the application of client submission, and the described application request of obtaining comprises user ID;
Formerly the behavioural information extraction module 202, and for extract the existing user behavior information of relative users from the user characteristics storehouse according to described user ID, described user behavior information comprises that the user is for the operation information of exemplary application formerly;
Applicating category determination module 203, for determining the applicating category of recommending to the user according to described user behavior information;
Coupling application acquisition module 204, for the application data sets at described applicating category, according to the user for application that formerly operation information of exemplary application extracts coupling;
In specific implementation, the embodiment of the present application can also comprise as lower module:
The behavioral statistics module, for gathering the user behavior information of submitting to after request is obtained in described application, write in the user characteristics storehouse by user ID.
As a kind of example of the concrete application of the embodiment of the present application, described user behavior information also comprises user's local operation behavioural information, and/or, user's online operation behavior information; In this case, described applicating category determination module 203 can comprise following submodule:
First Characteristic extracts submodule, for the local operation behavioural information from described user and/or online operation behavior information, extracts tag along sort and the first corresponding operation frequency;
The conversion submodule, for being converted to corresponding applicating category by described tag along sort by default correlation rule; The transformation rule that described default correlation rule is tag along sort and applicating category;
Second Characteristic extracts submodule, for from described user for the operation information of exemplary application formerly, extract user operated application message and second corresponding operation frequency in the Preset Time section, described application message comprises applicating category;
The sequence submodule, for according to the described first operation frequency and the second operation frequency, calculating the weight of each applicating category, sorted from high to low by the weight of described applicating category;
Classification is selected submodule, for the applicating category of front n applicating category for recommending to the user that extracts predetermined number; Wherein, described n is greater than 1 positive integer.
In a preferred embodiment of the present application, described device can also comprise as lower module:
The application data set generation module, for generating the application data set of each applicating category:
Described application data set generation module is connected with coupling application acquisition module 204, specifically can comprise following submodule:
Submodule is obtained in similar application, and for obtaining the application of same applicating category, described application has tag along sort;
The similarity calculating sub module, in described application, determining main application and application to be recommended, and calculate the similarity of application to be recommended and main application according to the tag along sort of each application;
Quality score parameter acquiring submodule, for obtaining the quality score parameter of described application to be recommended;
Application fetches submodule to be recommended, for extracting respectively the corresponding application to be recommended of same main application, sorted from high to low by similarity and the quality score parameter of each application to be recommended, and extract before predetermined number the application to be recommended of m; Wherein, described m is greater than 1 positive integer;
Application data set forms submodule, for main application and the correspondence application to be recommended of extracting being formed to the application data set of current application classification.
In a preferred embodiment of the present application, described coupling application acquisition module 204 can comprise following submodule:
Main applied statistics submodule, for according to the user for the operation information of exemplary application formerly, add up main application and the 3rd corresponding operation frequency, described master is applied as the operated application of user;
Submodule is determined in application to be recommended, for the application data sets at corresponding applicating category, application to be recommended according to described main application fetches coupling, and in the application to be recommended of described coupling, the application to be recommended that described the 3rd operation frequency is extracted respectively to some as the weight of application fetches, extract the application to be recommended that meets the first predetermined number altogether.
More preferably, described coupling application acquisition module 204 can also comprise following submodule:
Submodule is chosen in main application, for obtaining applicating category corresponding to main application, in same applicating category, by described the 3rd operation frequency, described main application is sorted, and extracts front k main application of predetermined number; Wherein, described k is greater than 1 positive integer;
Frequent 2 collection calculating sub module, match in twos for the main application by extracted, and calculates the described total degree that the main application of pairing occurs simultaneously in twos, generates frequent 2 collection;
Frequent 1 collection calculating sub module, the number of times occurred separately for calculating each main application, generate frequent 1 collection;
The confidence calculations submodule, for according to described frequent 2 collection and frequent 1 collection, calculating the degree of confidence of each main application, and sorted to main application by degree of confidence;
Submodule is determined in the coupling application, and for the application to be recommended that meets the first predetermined number by extracted, and the described main application by the degree of confidence sequence is mated, and generates the final coupling application of recommending.
The embodiment of the present application not only can be applied to, in the applied environment of single device, can also be applied to the applied environment of client-server, or further be applied in the applied environment based on cloud.
Due to described device embodiment substantially corresponding to preceding method embodiment, therefore not detailed part in the description of the present embodiment, can be referring to the related description in previous embodiment, at this, just do not repeated.In the application's device embodiment and system embodiment, related module, submodule and unit can be software, can be hardware, can be also the combination of software and hardware.
The application can be used in numerous general or special purpose computingasystem environment or configuration.For example: personal computer, server computer, handheld device or portable set, plate equipment, multicomputer system, the system based on microprocessor, set top box, programmable consumer-elcetronics devices, network PC, small-size computer, mainframe computer, comprise distributed computing environment of above any system or equipment etc.
The application can describe in the general context of the computer executable instructions of being carried out by computing machine, for example program module.Usually, program module comprises the routine carrying out particular task or realize particular abstract data type, program, object, assembly, data structure etc.Also can in distributed computing environment, put into practice the application, in these distributed computing environment, be executed the task by the teleprocessing equipment be connected by communication network.In distributed computing environment, program module can be arranged in the local and remote computer-readable storage medium that comprises memory device.
Finally, also it should be noted that, in this article, relational terms such as the first and second grades only is used for an entity or operation are separated with another entity or operational zone, and not necessarily requires or imply between these entities or operation the relation of any this reality or sequentially of existing.And, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thereby make the process, method, article or the equipment that comprise a series of key elements not only comprise those key elements, but also comprise other key elements of clearly not listing, or also be included as the intrinsic key element of this process, method, article or equipment.In the situation that not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within process, method, article or the equipment that comprises described key element and also have other identical element.
Above a kind of method and a kind of device of applying automatic recommendation of applying automatic recommendation that the application is provided is described in detail, applied specific case herein the application's principle and embodiment are set forth, the explanation of above embodiment is just for helping to understand the application's method and core concept thereof; Simultaneously, for one of ordinary skill in the art, the thought according to the application, all will change in specific embodiments and applications, and in sum, this description should not be construed as the restriction to the application.
Claims (10)
1. the method that application is recommended automatically, is characterized in that, comprising:
Receive the user and obtain request from the application of client submission, the described application request of obtaining comprises user ID;
Extract the existing user behavior information of relative users according to described user ID from the user characteristics storehouse, determine the applicating category of recommending to the user according to described user behavior information;
Extract the application of coupling is recommended in the application data sets of described applicating category.
2. the method for claim 1, is characterized in that, also comprises:
Gather the user behavior information of submitting to after request is obtained in described application, write in the user characteristics storehouse by user ID.
3. the method for claim 1, is characterized in that, described user behavior information also comprises user's local operation behavioural information, and/or, user's online operation behavior information;
Describedly according to user behavior information, determine that the step of the applicating category of recommending to the user comprises:
From described user's local operation behavioural information and/or online operation behavior information, extract tag along sort and the first corresponding operation frequency;
Described tag along sort is converted to corresponding applicating category by default correlation rule; The transformation rule that described default correlation rule is tag along sort and applicating category;
Operation information from described user for exemplary application formerly, extract user operated application message and second corresponding operation frequency in the Preset Time section, described application message comprises applicating category;
Calculate the weight of each applicating category according to the described first operation frequency and the second operation frequency, sorted from high to low by the weight of described applicating category;
Front n the applicating category that extracts predetermined number is the applicating category of recommending to the user; Wherein, described n is greater than 1 positive integer.
4. method as claimed in claim 3, is characterized in that, generates the application data set of certain applicating category by following steps:
Obtain the application of same applicating category, described application has tag along sort;
Determine main application and application to be recommended in described application, and calculate the similarity of application to be recommended and main application according to the tag along sort of each application;
Obtain the quality score parameter of described application to be recommended;
Extract respectively the corresponding application to be recommended of same main application, sorted from high to low by similarity and the quality score parameter of each application to be recommended, and extract before predetermined number the application to be recommended of m; Wherein, described m is greater than 1 positive integer;
The application data set that main application and the application to be recommended of the correspondence extracted is formed to the current application classification.
5. method as claimed in claim 4, is characterized in that, the described application data sets at applicating category comprises for the step of the formerly application of the operation information extraction coupling of exemplary application according to the user:
For the operation information of exemplary application formerly, add up main application and the 3rd corresponding operation frequency according to the user, described master is applied as the operated application of user;
Application data sets at corresponding applicating category, application to be recommended according to described main application fetches coupling, and in the application to be recommended of described coupling, the application to be recommended that described the 3rd operation frequency is extracted respectively to some as the weight of application fetches, extract the application to be recommended that meets the first predetermined number altogether.
6. method as claimed in claim 5, is characterized in that, the described application data sets at applicating category also comprises for the step of the formerly application of the operation information extraction coupling of exemplary application according to the user:
Obtain applicating category corresponding to main application, in same applicating category, by described the 3rd operation frequency, described main application is sorted, extract front k main application of predetermined number; Wherein, described k is greater than 1 positive integer;
Extracted main application is matched in twos, calculate the described total degree that the main application of pairing occurs simultaneously in twos, generate frequent 2 collection;
Calculate the number of times that each main application occurs separately, generate frequent 1 collection;
Calculate the degree of confidence of each main application according to described frequent 2 collection and frequent 1 collection, and by degree of confidence, main application is sorted;
By the extracted application to be recommended that meets the first predetermined number, and the described main application by the degree of confidence sequence is mated, and generates the final coupling application of recommending.
7. the device that application is recommended automatically, is characterized in that, comprising:
The request receiving module, obtain request for receiving the user from the application of client submission, and the described application request of obtaining comprises user ID;
Behavioural information extraction module formerly, for extracting the existing user behavior information of relative users from the user characteristics storehouse according to described user ID;
The applicating category determination module, for determining the applicating category of recommending to the user according to described user behavior information;
Coupling application acquisition module, the application of extracting coupling for the application data sets at described applicating category;
The application recommending module, recommended for the application by described coupling.
8. device as claimed in claim 7, is characterized in that, also comprises:
The behavioral statistics module, for gathering the user behavior information of submitting to after request is obtained in described application, write in the user characteristics storehouse by user ID.
9. device as claimed in claim 7, is characterized in that, described user behavior information also comprises user's local operation behavioural information, and/or, user's online operation behavior information;
Described applicating category determination module comprises:
First Characteristic extracts submodule, for the local operation behavioural information from described user and/or online operation behavior information, extracts tag along sort and the first corresponding operation frequency;
The conversion submodule, for being converted to corresponding applicating category by described tag along sort by default correlation rule; The transformation rule that described default correlation rule is tag along sort and applicating category;
Second Characteristic extracts submodule, for from described user for the operation information of exemplary application formerly, extract user operated application message and second corresponding operation frequency in the Preset Time section, described application message comprises applicating category;
The sequence submodule, for according to the described first operation frequency and the second operation frequency, calculating the weight of each applicating category, sorted from high to low by the weight of described applicating category;
Classification is selected submodule, for the applicating category of front n applicating category for recommending to the user that extracts predetermined number; Wherein, described n is greater than 1 positive integer.
10. device as claimed in claim 9, is characterized in that, also comprises:
The application data set generation module, for generating the application data set of each applicating category: specifically comprise:
Submodule is obtained in similar application, and for obtaining the application of same applicating category, described application has tag along sort;
The similarity calculating sub module, in described application, determining main application and application to be recommended, and calculate the similarity of application to be recommended and main application according to the tag along sort of each application;
Quality score parameter acquiring submodule, for obtaining the quality score parameter of described application to be recommended;
Application fetches submodule to be recommended, for extracting respectively the corresponding application to be recommended of same main application, sorted from high to low by similarity and the quality score parameter of each application to be recommended, and extract before predetermined number the application to be recommended of m; Wherein, described m is greater than 1 positive integer;
Application data set forms submodule, for main application and the correspondence application to be recommended of extracting being formed to the application data set of current application classification.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310347185.9A CN103455559B (en) | 2011-12-27 | 2011-12-27 | A kind of method and device applying recommendation automatically |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310347185.9A CN103455559B (en) | 2011-12-27 | 2011-12-27 | A kind of method and device applying recommendation automatically |
| CN 201110444798 CN102567511B (en) | 2011-12-27 | 2011-12-27 | Method and device for automatically recommending application |
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| Application Number | Title | Priority Date | Filing Date |
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| CN 201110444798 Division CN102567511B (en) | 2011-12-27 | 2011-12-27 | Method and device for automatically recommending application |
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