CN102567511B - Method and device for automatically recommending application - Google Patents
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Abstract
The invention provides a method and a device for automatically recommending an application. The method comprises the steps of: receiving an application acquisition request submitted from a client by a user, wherein the application acquisition request comprises a user identification; according to the user identification, extracting the existing user behavior information of corresponding user from a user feature library, wherein the user behavior information comprises operation information that a user aims at previously recommended application; according to the user behavior information, determining the type of the application recommended to the user; in an application data set of the type of the application, according to the operation information that the user aims at the previously recommended application, extracting an matched application; and according to the type of the application, generating a corresponding application folder, and placing the matched application in the corresponding application folder for recommending. According to the invention, the individual needs of users can be satisfied, the recommending efficiency is improved and the coverage rate is increased.
Description
Technical field
The application relates to technical field of information processing, particularly relates to a kind of method and a kind of device of using automatic recommendation of using automatic recommendation.
Background technology
The internet is the important channel that people obtain information, when the principal feature of tradition internet is that the user seeks own interested things, need carry out a large amount of search by browser, need the 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, the demand that people use (Application) to diverse network also more and more widely, 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 of finishing client.
Although occurred the concept of so-called " thin-client (Thin Client) " now, thin-client is sent to server process with 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, more is to be applied in the commercial LAN (Local Area Network) of enterprise-level, also is not suitable for the amusement demand of domestic consumer at present.
For making the user obtain better experience, the scheme that provides interested application to recommend automatically for the user has been provided prior art, namely 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 defective in the manual mode of recommending of this editorial staff:
1, efficient is low excessively, and is too low for the recommendation coverage rate of using, and for example, for application hundreds thousand of on the platform, adopts artificial the recommendation every day, also can only recommend hundreds of individual.If want to recommend whole application in fact can't realize, and coverage rate is too low, because proportion is too low.
2, this recommendation unified principle of recommending that is based on the editorial staff is fully carried out, and is the same for each user, can't satisfy the demand of user individual.Because the application of some recommendation is suitable for the certain user, and dislikes for the certain user.
Therefore, need the urgent technical matters that solves of those skilled in the art to be exactly at present: to propose a kind of mechanism of using automatic recommendation, satisfying user's individual demand, and improve recommendation efficient and coverage rate.
Summary of the invention
The application's technical matters to be solved provides a kind of method of using automatic recommendation, in order to satisfying user's individual demand, and improves and recommends efficient and coverage rate.
The application also provides a kind of device of using 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 using automatic recommendation, comprising:
Receive the user and obtain request from the application of client submission, described application is obtained and is comprised user ID in the request;
From the user characteristics storehouse, extract the existing user behavior information of relative users according to described user ID, described user behavior information comprises that the user is at the operation information of exemplary application formerly, the local operation behavioural information that also comprises the user, and/or, user's online operation behavior information; Have tag along sort information in described user's local operation behavioural information and/or the online operation behavior information;
Determine to the applicating category of user's recommendation according to described user behavior information;
In the application data sets of described applicating category, extract the application of coupling at the operation information of exemplary application formerly according to the user;
Generate corresponding application file folder by described applicating category, corresponding application file folder is put in the application of described coupling recommended;
Wherein, describedly determine that according to user behavior information the step of the applicating category recommended 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; Described default correlation rule is the transformation rule of tag along sort and applicating category;
From the operation information of described user at exemplary application formerly, extract user operated application message and second corresponding operation frequency in the Preset Time section, comprise applicating category in the described application message;
Calculate the weight of each applicating category according to the described first operation frequency and the second operation frequency, sort from high to low by the weight of described applicating category;
Preceding n the applicating category that extracts predetermined number is the applicating category of recommending to the user; Wherein, described n is the positive integer greater than 1;
If extract preceding n applicating category of not enough predetermined number, then the applicating category of the applicating category that the actual access times of adding up according to high in the clouds of the network user are maximum or up-to-date setting carries out polishing as the applicating category of recommending to the user.
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, 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;
In described application, determine main the application and application to be recommended, and calculate application to be recommended and the main similarity of using according to the tag along sort of each application;
Obtain described application quality grading parameters to be recommended;
Extract the same main corresponding application to be recommended of using respectively, sort from high to low by similarity and the quality score parameter of each application to be recommended, and extract before the predetermined number m application to be recommended; Wherein, described m is the positive integer greater than 1;
The application data set of the main application corresponding to be recommended of using and extracting being formed the current application classification.
Preferably, described application data sets at applicating category can comprise at the step of the application of the operation information extraction coupling of exemplary application formerly according to the user:
At the operation information of exemplary application formerly, statistics is main to be used and the 3rd corresponding operation frequency according to the user, and 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, with the application to be recommended that described the 3rd operation frequency is extracted some respectively as the weight of application fetches, extract the application to be recommended of satisfying first predetermined number altogether.
Preferably, described application data sets at applicating category can also comprise at the step of the application of the operation information extraction coupling of exemplary application formerly according to the user:
Obtain the main corresponding applicating category of using, in same applicating category, by described the 3rd operation frequency described main the application sorted, extract preceding k main application of predetermined number; Wherein, described k is the positive integer greater than 1;
The main application in twos of extracting matched, calculate the main total degree that occurs simultaneously of using of described pairing in twos, generate frequent 2 collection;
Calculate each main number of times that occurs separately of using, generate frequent 1 collection;
Calculate each main degree of confidence of using according to described frequent 2 collection and frequent 1 collection, and by degree of confidence main the application sorted;
With the application to be recommended of satisfying first predetermined number of extracting, and described main application the by the degree of confidence ordering mated, and generates final coupling of recommending and uses.
The embodiment of the present application discloses a kind of device of using automatic recommendation simultaneously, comprising:
The request receiver module is used for receiving the user and obtains request from the application of client submission, and described application is obtained and comprised user ID in the request;
Behavioural information extraction module formerly, be used for extracting 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 at the operation information of exemplary application formerly, the local operation behavioural information that also comprises the user, and/or, user's online operation behavior information; Have tag along sort information in described user's local operation behavioural information and/or the online operation behavior information;
The applicating category determination module is used for determining to the applicating category of user's recommendation according to described user behavior information;
Coupling is used acquisition module, is used for the application data sets at described applicating category, the application of extracting coupling at the operation information of exemplary application formerly according to the user;
Use recommending module, be used for generating corresponding application file folder by described applicating category, corresponding application file folder is put in the application of described coupling recommended;
Wherein, described applicating category determination module comprises:
The first feature extraction submodule is used for local operation behavioural information and/or online operation behavior information from described user, extracts tag along sort and the first corresponding operation frequency;
The conversion submodule is used for described tag along sort is converted to corresponding applicating category by default correlation rule; Described default correlation rule is the transformation rule of tag along sort and applicating category;
The second feature extraction submodule is used for from described user at the operation information of exemplary application formerly, extracts user operated application message and second corresponding operation frequency in the Preset Time section, comprises applicating category in the described application message;
The ordering submodule is used for calculating according to the described first operation frequency and the second operation frequency weight of each applicating category, sorts from high to low by the weight of described applicating category;
Classification is selected submodule, and preceding n the applicating category that is used for the extraction predetermined number is the applicating category of recommending to the user; Wherein, described n is the positive integer greater than 1;
Applicating category polishing module, be used for when extracting preceding n applicating category of not enough predetermined number, then the applicating category of the applicating category that the actual access times of adding up according to high in the clouds of the network user are maximum or up-to-date setting carries out polishing as the applicating category of recommending to the user.
Preferably, described device also comprises:
The behavioral statistics module is used for gathering the user behavior information of submitting to after request is obtained in described application, writes in the user characteristics storehouse by user ID.
Preferably, described device can also comprise:
The application data set generation module be used for to generate the application data set of each applicating category: specifically comprise:
Submodule is obtained in similar application, and for the application of obtaining same applicating category, described application has tag along sort;
The similarity calculating sub module is used for determining main the application and application to be recommended in described application, and calculates application to be recommended and the main similarity of using according to the tag along sort of each application;
Quality score parameter acquiring submodule is used for obtaining described application quality grading parameters to be recommended;
Application fetches submodule to be recommended is used for extracting respectively the same main corresponding application to be recommended of using, and sorts from high to low by similarity and the quality score parameter of each application to be recommended, and extracts before the predetermined number m application to be recommended; Wherein, described m is the positive integer greater than 1;
Application data set forms submodule, is used for leading the application data set that the current application classification is formed in the application corresponding to be recommended of using and extracting.
Preferably, described coupling application acquisition module can comprise:
Main applied statistics submodule is used for according to the user at the operation information of exemplary application formerly, and statistics is main to be used and the 3rd corresponding operation frequency, and described master is applied as the operated application of user;
Submodule is determined in application to be recommended, be used 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, with the application to be recommended that described the 3rd operation frequency is extracted some respectively as the weight of application fetches, extract the application to be recommended of satisfying first predetermined number altogether.
Preferably, described coupling application acquisition module can also comprise:
The main application chosen submodule, is used for obtaining the main corresponding applicating category of using, and in same applicating category, by described the 3rd operation frequency described main the application sorted, and extracts preceding k main application of predetermined number; Wherein, described k is the positive integer greater than 1;
Frequent 2 collection calculating sub module are used for main the application in twos of extracting matched, and calculate the main total degree that occurs simultaneously of using of described pairing in twos, generate frequent 2 collection;
Frequent 1 collection calculating sub module is used for calculating each main number of times that occurs separately of using, and generates frequent 1 collection;
The confidence calculations submodule is used for calculating each main degree of confidence of using according to described frequent 2 collection and frequent 1 collection, and by degree of confidence main the application is sorted;
Coupling is used and is determined submodule, the application to be recommended of satisfied first predetermined number that is used for extracting, and described main application the by the degree of confidence ordering mated, and generates the coupling of finally recommending and uses.
Compared with prior art, the application has the following advantages:
The application is based on the application of having recommended to the user, analysis user is at the operation information of described exemplary application formerly, 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, according to the operation information of above-mentioned user at described exemplary application formerly, 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 the file of corresponding applicating category recommends, thereby between application and user, set up contact, fully satisfy user's individual demand, and effectively improved recommendation efficient and the coverage rate used.
Moreover the application as entrance, is directly passing through the application file clip icon to user's exemplary application, so that the faster easier required application of obtaining of user has made things convenient for user's operation on the interface or by the link on the interface with user interface; And, can point out the user use to this application by icon as the mode of using entrance, but before the user really selected to use, actual installation should be used corresponding configuration file, like this, can before use and exceed and take client resource.In addition, the icon in the user interface can be concentrated by the network side central server and dispose or push, and this has just prevented that rogue program from arbitrarily adding the malice icon in the interface, further improved security.
Description of drawings
Fig. 1 is a kind of flow chart of steps of using the method embodiment of automatic recommendation of the application;
Fig. 2 is a kind of structured flowchart of using 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, based on the application of having recommended to the user, analysis user is at the operation information of described exemplary application formerly, 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, according to the operation information of above-mentioned user at described exemplary application formerly, 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 are put into the file of corresponding applicating category and recommend, thereby between application and user, set up contact.
With reference to Fig. 1, a kind of flow chart of steps of using the method embodiment of automatic recommendation that it shows the application specifically can comprise the steps:
In specific implementation, the user starts client can trigger the request of obtaining of using, 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 the 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 at the operation information of exemplary application formerly.Described user's local operation behavioural information and online operation behavior information can have tag along sort (tag) information usually, for example, for the video that the user opens at local operation, have classification label informations such as the fiery shadow person of bearing, animation, serial, illusion, risk, bank Ben Qishi; Or as, for the network address that the user visits, have the classification label informations such as king of video, film, comedy movie, comedy on the net.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 that is installed on the 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 is gathered the online operation behavior information of user in a period of time by browser, comprises the network address of visit and corresponding access times etc.;
As the online operation behavior information of gathering by browser in the user 15 days be:
Example 2, by being installed in the local operation behavioural information of the fail-safe software collection user on the subscriber equipment, as online operation behavior information and the local behavioural information of gathering by 360 net shields in the user 15 days be: open MPC and number of times thereof, open certain recreation and number of times thereof etc.
Certainly, all only as example, it all is feasible that those skilled in the art adopt any mode to gather required user behavior information according to actual conditions for the method for above-mentioned collection and the information of collection, and the embodiment of the present application need not this to be limited.
In a preferred embodiment of the present application, described step 103 specifically can comprise following substep:
Substep 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;
Substep S12, described tag along sort is converted to corresponding applicating category by default correlation rule; Described default correlation rule is the transformation rule of tag along sort and applicating category;
Substep S13, from the operation information of described user at exemplary application formerly, extract user operated application message and second corresponding operation frequency in the Preset Time section, comprise applicating category in the described application message;
Substep S14, calculate the weight of each applicating category according to the described first operation frequency and the second operation frequency, sort from high to low by the weight of described applicating category;
Preceding n applicating category of substep S15, extraction predetermined number is the applicating category of recommending to the user; Wherein, described n is the positive integer greater than 1.
In practice, can by the analysis user behavioural information, obtain the applicating category of user behavior information conforms according to setting in advance applicating category by the technician.For example, the application file folder basic classification that sets in advance has 20, and by the analysis user behavioural information, it is unwanted for the active user that discovery has some basic classification, then can divide applicating category that user behavior information belongs to and be 3 or 5 of behavioural habits before being close to the users more.For example, video, recreation, education etc.
For making those skilled in the art understand the application better, below determine to the process of the applicating category of user's recommendation according to user behavior information by a concrete example explanation:
Data Source:
(1) nearest 15 days net shield data: Data11;
(2) nearest 15 days user uses application interpolation or the click logs of safety desktop: Data12;
The data layout of Data11 is:
The user ID Mid tag along sort Interest 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；MSIE?7.0；Windows?NT?6.1；Trident/4.0；GTB7.1；SLCC2；.NET?CLR?2.0.50727；.NET?CLR?3.5.30729；.NET?CLR?3.0.30729；Media?Center?PC?6.0)″
Step1: will from net shield data Data11, extract tag along sort Interest, be converted to the basic classification that application file presss from both sides the user interest under the taxonomic hierarchies by preset conversion rule table (yunCatToZhuoMianCat.conf), be about to described class label and be converted to corresponding applicating category.Can comprise in the described preset conversion rule table yunCatToZhuoMianCat.conf form: the information of tag along sort, applicating category title AppName and applicating category sign Appid, as shown in the table:
The result of Data11 conversion is as shown in the table:
| Mid | Interest | AppName | Appid | Weight1 |
| 000020218613d5fc8e05c314dba32956 | comic-dm | The fashion amusement | 8 | 4 |
| 0000175873530b93d848614a0c188c5b | novel-dm | Novel | 11 | 1 |
| 00002e3bb9037870973b328078971c98 | 4399-dm | Recreation | 5 | 1 |
Step2: by resolving original log Data12, calculate each Mid clicked or added each application in nearest 15 days the operation frequency (the second operation frequency), 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:
The single tractor of other chess and card cartoon English of 100,026,002 5 trivial games
100,013,330 4 TV play ancient costume story of a play or opera palace schemings
100,114,314 6 healthy home doctor autodiagnosis family doctors
The cook way of pork braised in brown sauce of 100,114,370 6 cuisines pork braised in brown sauce menu recipes
The military suspense confidential informant of 100,013,349 4 TV play
If by resolving original log Data12, the data of calculating each Mid clicked or added each application in nearest 15 days the operation frequency (second operates frequency Weight2) are as shown in the table:
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:
| Mid | fenleiid | Weight2 |
| 00008fc5c27c3354e1e0c9b6b7527dd9 | 5 | 1 |
| 0000b5d11c0c8ea46817fc32f467c3ba | 4 | 3 |
| 0001555e4ea2b299b6fbc55f46eeb771 | 6 | 4 |
Step3: the result of Step1 and Step2 is weighted on average according to the first operation frequency and the second operation frequency, sort according to final score then, 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 ...;
Step2 result is: type1 behavior N1 time, type2 clicks N2 time, type3 click N3 time ... score1=n1*0.6+N1*0.4 then, score2=n2*0.6+N2*0.4, score3=n3*0.6+N3*0.4......
Sort by score, get the applicating category of preceding 9 applicating category for recommending to the active user.
In specific implementation, if being analyzed the applicating category of dividing, user behavior information can't reach specified quantity, if example can only generate three classifications in the employing, can't satisfy the demand of 9 applicating categories, then can carry out polishing as the applicating category of recommending according to the applicating category of the maximum applicating category of the actual access times of the network user that high in the clouds is added up or up-to-date setting.
Certainly, the method of above-mentioned division user behavior information institute belonging kinds is only as example, it all is 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 with 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 user's employed various services on network, as application program, webpage, video, novel, music, recreation, 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 the application data set of certain applicating category by following substep:
Substep S21, the application of obtaining same applicating category, described application has tag along sort;
Substep 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;
Substep S23, obtain described application quality grading parameters to be recommended;
Substep S24, extract the same main corresponding application to be recommended of using respectively, sort from high to low by similarity and the quality score parameter of each application to be recommended, and extract before the predetermined number m application to be recommended; Wherein, described m is the positive integer greater than 1;
Substep S25, will lead the application data set that the current application classification is formed in the application corresponding to be recommended of using and extracting.
Above preferred embodiment namely at 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 the application better, below by a concrete example process of above-mentioned generation application data set is described.
1). according to the similarity between the tag along sort tag computing application app of the applicating category of using and 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 makes up in twos, calculates its similarity, and computing formula is: Similarity=i/ (n1+n2-i); Wherein, n1 is the number of app1 (using 1) back tag, and n2 is the number of app2 (using 2) back tag; The i minimum is 2, is n1 to the maximum, carries out searching loop.For example:
The input data are:
100,030,071 4 other 2010 continents of film story of a play or opera comedy love monarch Wu Chen Liu Yanjun Xie Xiaoming
Shining other 2009 Korea S of Shen Tailuo of 100,030,073 4 film story of a play or opera comedies action Jin Henajiang will
Other 2009 U.S. of the triumphant strange money De Lekanteburui Alex Pu Luoyasi of the terrible Nicholas of 100,030,074 4 film suspense science fictions
Destination file Data1 is:
100030071 100030073 0.25
100030071 100030074 0.11
100030073 100030074 0.11
2). in same main Appid, undertaken integrated ordered to Appid to be recommended by the quality score of similarity and Appid (every day download, user's scoring), be Appid similarity * similarity weight+Appid quality score * (1-similarity weight), the highest preceding 50 Appid to be recommended of intercepting integrate score merge into delegation then;
Input data: Data1 (destination file of 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:
100030071 100030073 0.25
100030071 100030074 0.11
100030073 100030074 0.11
100030073 100030071 0.25
100030074 100030071 0.11
100030074 100030073 0.11
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 substep:
Substep S31, according to the user at the operation information of exemplary application formerly, statistics is main to be used and the 3rd corresponding operation frequency, described master is applied as the operated application of user;
Substep 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, with the application to be recommended that described the 3rd operation frequency is extracted some respectively as the weight of application fetches, extract the application to be recommended of satisfying first predetermined number altogether.
For making those skilled in the art understand the application better, below by a concrete example above-mentioned substep S31-S32 is described.
Step1：
3). according to the application operating behavior daily record of Mid, statistics Mid adds or clicks the number of times of each app;
Input data: Mid adds or clicks the daily record of using and record (nearest 30 days application operating behavior daily record);
The form of output data Data3 is: Mid master Appid (id of the app of 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＝1.4.0.1041?HTTP/1.1″200?0?″-″″Mozilla/4.0(compatible；MSIE?7.0；Windows?NT?6.0；SLCC1；.NET?CLR2.0.50727；Media?Center?PC?5.0；.NET?CLR?3.5.30729；.NET?CLR?3.0.30618)″
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＝1.4.0.1040?HTTP/1.1″200?0″-″″Mozilla/4.0(compatible；MSIE?6.0；Windows?NT?5.1；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；MSIE?6.0；Windows?NT?5.1)″
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＝1.4.0.1041?HTTP/1.1″200?0″-″″Mozilla/4.0(compatible；MSIE?7.0；Windows?NT?5.1；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″200?0″-″″Mozilla/4.0(compatible；MSIE?6.0；Windows?NT?5.1)″
Output data Data3 is:
0004a218f3b8a96b59f67a8f14be5e98 100000289 102
0004ce91dc7726afdc420a97ad050f5a 100103087 1
0004cfe7c72aa83bfa20e561b6a00823 102020214 2
0004ec5290131cf7fa6d5a18833e06a5 102005903 3
0004f55c24b9fb8745b6d7595d94972d 120033743 1
00051004579f67de2066dc94f8952fd4 100000275 5
00054c7ba3cf1b45330444cbb737cac4 100114758 4
4). according to Data2 and Data3, mate by main app, sort according to weight then
Mid1?fenleiid1?Appid1?Appid2?Appid3?Appid4......weight
Mid1?fenleiid2?Appid11?Appid22?Appid33?Appid44......weight
According to the weight size, weight is more big, and intercepting app is more many in such the inside, adopts the mode of intercepting at random, intercepts 50 Appid altogether, is that key word merges with Mid and fenleiid then, 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 the above-mentioned Data2 bracket is preceding 50 Appids the highest with main app similarity
Data3 is:
Xx1?00?15
Xx1?002?3
Xx1?003?2
Xx1?008?5
By the Appid_name classification table of comparisons, map out its classification, acquisition Data33 is:
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 the data;
Article 2, randomly draw 20=6 Appid of 3/ (5+3+2) * in the data;
Article 3, randomly draw 20=4 Appid of 2/ (5+3+2) * in the data;
Final output data are:
Xx1?1?100?101?102?103?104?105?106?107?108?109?204?205?206?207?208?209306?307?308?303
Xx2?2?801?802?803?804?805?806?807?808?809?810......
More preferably, described step 104 may further include following substep:
Substep S33, obtain the main corresponding applicating category of using, in same applicating category, by described the 3rd operation frequency described main the application sorted, extract preceding k main application of predetermined number; Wherein, described k is the positive integer greater than 1;
Substep S34, main use pairing in twos with what extract, calculates the main total degree of appearance simultaneously of using of described pairing in twos, generate frequent 2 collection;
Substep S35, calculate each main number of times that occurs separately of using, generate frequent 1 collection;
Substep S36, calculate each main degree of confidence of using according to described frequent 2 collection and frequent 1 collection, and by degree of confidence main the application sorted;
Substep S37, with the application to be recommended of satisfying first predetermined number of extracting, and described main application the by the degree of confidence ordering mated, and generates final coupling of recommending and uses.
One of core idea of present embodiment is that adding or click the behavior of using based on the user is that it finds similar application to the preference of using, and according to user's the i.e. historical application of adding or clicking of historical preference, recommends similar application to it then.From the angle of calculating, exactly all users are come similarity between the computing application to the preference of certain application as a vector, after the similar application that is applied, predict that according to the preference of user's history the active user does not also represent the application of preference, calculates the list of application of an ordering as recommendation.
For making those skilled in the art understand the application better, below by a concrete example above-mentioned substep S33-S37 is described.
Step2：
1). according to the behavior daily record that Mid adds and click application, add up the number of times that Mid adds or click each app;
Input data: Mid adds or clicks and use daily record (nearest 30 days application operating behavior daily record);
The form of output data Data1 is: Mid master Appid (app that clicks or add)
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＝1.4.0.1041?HTTP/1.1″2000″-″″Mozilla/4.0(compatible；MSIE?7.0；Windows?NT?6.0；SLCC1；.NET?CLR2.0.50727；Media?Center?PC?5.0；.NET?CLR?3.5.30729；.NET?CLR?3.0.30618)″
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＝1.4.0.1040?HTTP/1.1″200?0″-″″Mozilla/4.0(compatible；MSIE?6.0；Windows?NT?5.1；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；MSIE?6.0；Windows?NT?5.1)″
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＝1.4.0.1041?HTTP/1.1″200?0″-″″Mozilla/4.0(compatible；MSIE?7.0；Windows?NT?5.1；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″200?0″-″″Mozilla/4.0(compatible；MSIE?6.0；Windows?NT?5.1)″
Output data Data1 is:
00062ee80feec92758b8be8d3e4b9c16 100113997 2
0006d880a38f687c3fca10e8c4efa227 102019670 2
0007400ba8300fc80b7210a0e66de257 102022805 2
0007bda041324ee53b83f0343daf84d2 102005801 2
00080118032d30ad28a9bce8232d17c8 100013133 3
0008020587ea4fecf08ad58b09ef5904 110195914 2
00082bf0489199360bce8a06693ef3f5 100115004 3
Shine upon with the Appid_name table of comparisons, find out the applicating category of each Appid correspondence, the result is as follows:
00062ee80feec92758b8be8d3e4b9c16 1 100113997 2
0006d880a38f687c3fca10e8c4efa227 1 102019670 2
0007400ba8300fc80b7210a0e66de257 2 102022805 2
0007bda041324ee53b83f0343daf84d2 9 102005801 2
00080118032d30ad28a9bce8232d17c8 6 100013133 3
0008020587ea4fecf08ad58b09ef5904 6 110195914 2
00082bf0489199360bce8a06693ef3f5 4 100115004 3
2). in same Mid and feileid, its click or the app that added by clicking or adding number of times and sort, are got preceding 20 Appid, and are included into delegation.
Input data: Data1 (the output data of previous step)
The data structure of output data Data2 is: Mid fenleiid Appid1 weight1 Appid2weight2 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 1100000913 1
007abe31b117a554df0fefc2a91200c2 2 120042762 11?102023358 2100000568 2 102010364 1 100115004 1
007b3dc8a31a6627a6e2468f789aa078 8 102007826 19?102020628 6102007664 6 102028968 4 100012183 3 100012315 3102043563 2 102022076 2 102000032 2 102031006 2110004672 2 102007377 2 101000009 2 110091072 2100030320 2 102044509 2 102043791 2 102044243 2102044665 2 100040423 2
007bb8043a487ed3a690aa6d461a3c10?10?102019572 3
007d072a6bfe28591f0eb4c5d533784c?18?100000525?31?10000028919?101000053?16?102005903 6 100000625 4 102020030 4102020628 3 120055003 3 100000913 3 102001686 2100034506 2 102005985 2 100000801 2 102044292 2110153628 2 100115575 2 100045261 2 100115650 2100103773 2 100102029 2
3). in a pair of Mid and fenleiid, Appid matches in twos, and note is common to be occurred 1 time, is 1 class with two Appid then, calculates two total degrees that Appid occurs simultaneously, generates frequent 2 collection.
Input data: Data2 (the output data of 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:
001?002?1
001?003?1
001?004?1
002?003?1
002?004?1
003?004?1
001?002?1
002?005?1
002?004?1
Output data Data3 is:
001?002?2
001?003?1
001?004?1
002?003?2
002?004?2
003?004?1
4). calculate frequent 1 collection, note is calculated the number of times that each Appid occurs
The output data in input data: Data1 (the 1st) step)
The data structure of output data Data4 is: Appid weight (occurrence number)
For example:
The input data:
Xx1?001?10
Xx1?002?8
Xx1?003?5
Xx2?002?8
Xx2?003?9
Xx3?003?7
Output data Data4 is:
001?1
002?2
003?3
5). calculate degree of confidence, and sort 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, then with respect to A, the degree of confidence of B is N/M, sorts according to degree of confidence, gets the application of ordering preceding 50, and be applied as class with head, and merging to delegation, structure is:
A?B C?D ......
Input data: Data3 and Data4
The data structure of output data Data5 is: Appid Appid1 weight1 Appid2weight2......Appid50 weight50
For example:
Input data Data3 is:
001?100?20
001?101?18
001?102?16
001?103?14
001?104?12
001?106?10
002?201?50
002?202?30
002?101?10
002?102?5
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, mate according to user Appid, generate recommendation results.
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......
Mate according to the Appid that indicates underscore, 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, sorting according to weight, to get preceding 50 application as follows:
Xx1?1?100?101?102?103?104?105?106?107......200?201?202?203?204?205206?207......
In specific implementation, can be further the result of Step1 and Step2 in the above-mentioned example be merged according to Mid and fenleiid, the applicating category of determining with step 103 filters, only get the application data of definite applicating category, in each applicating category, get 50 application then at random as recommendation results.If do not have data in certain applicating category, then can adopt arbitrary mechanism to carry out polishing, for example, put into the most popular application, up-to-date application etc.
Certainly, the above-mentioned method of using with the user behavior information matches of searching only is used as example, it also is feasible that those skilled in the art adopt other computing method, for example, tag along sort by calculating user behavior information and the matching degree of the tag along sort of respective classes application data sets application etc., the application need not this to be limited.
Use the embodiment of the present application, category is generated the application file folder, under the respective classes, namely in the application file folder of corresponding classification, recommend to the user with the application of user behavior information matches, thus the resource that is conducive to save subscriber equipment.
In specific implementation, the application file folder for recommending the user can represent in the different split screens of desktop, preferably, can also be according to height and the width of user's split screen, determine the number of the application file folder recommended in each split screen.Use the embodiment of the present application, the order that represents of described application file folder is that the operation frequency according to the corresponding Main classification label of each applicating category arranges from high to low, so the application file folder is that matching degree according to user interest represents from high to low to the user; And the application in the application file folder is also sorted by weight, namely also is that the matching degree according to user interest represents from high to low to the user, thereby operation that can more convenient user makes the user obtain better experience.
In specific implementation, can unify in the user interface of terminal desktop to show the icon corresponding with a plurality of application file folders that each icon represents an application file folder, by icon conduct and the mode of using 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 the user interface comprises " video ", " novel ", " education " and " recreation ", click the icon of " video " application file folder the user after, enter the subwindow of this application file folder, showing in subwindow has a plurality of application icons such as TV play, film, animation, variety.Can point out the user use to this application by icon as the mode of using entrance, but before the user really selected to use, actual installation should not used corresponding configuration file, like this, not only can be user-friendly, and before use and exceed and take client resource.
Icon in the user interface can be concentrated by the network side central server and dispose or push, and this has just prevented that rogue program from arbitrarily adding the malice icon in the interface, further improved security.There is the configuration file of central server centralized management can comprise the corresponding reference address of using, present specification, and the unfolding mode of described application, perhaps their any combination.
For example, use for web, the address of web visit is sent to end side by the mode of central server by configuration file, and this has just prevented rogue program the distorting reference address of end side.
And, the network side central server can be by the profile information that upgrades with the mutual acquisition of third party's content server, for example, if the reference address of certain application changes, server can be by the address information after upgrading with the mutual acquisition of content server, and send over by configuration file, stopped to change the opportunity that stays to rogue program because of reference address.
In addition, subscriber equipment can also upgrade the display state of this icon, with further prompting user behind the configuration file that obtains the application corresponding with described icon.For example, do not obtain configuration file before, icon can be black and white, or dark-coloured, and after acquisition, can become colour or light tone.
It should be noted that also the application file clip icon of showing can be one or more in the end side user interface, 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, and wherein any one is used when obtaining lastest imformation, all can obtain prompting at this entrance icon place.
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, then user behavior information can be unified in server end or high in the clouds is handled, in such an embodiment, can in the user characteristics storehouse, work as inferior operation behavior information by recording user, and determine to press from both sides to the application file that the user recommends to reach accordingly to use according to the previous operation behavior information in user characteristics storehouse.
Need to prove, for method embodiment, for simple description, so it all is expressed as a series of combination of actions, but those skilled in the art should know, the application is not subjected 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 the 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 using the device embodiment of automatic recommendation of the application, specifically can comprise as lower module:
Formerly the behavioural information extraction module 202, are used for extracting the existing user behavior information of relative users from the user characteristics storehouse according to described user ID, and described user behavior information comprises that the user is at the operation information of exemplary application formerly;
Applicating category determination module 203 is used for determining to the applicating category of user's recommendation according to described user behavior information;
Coupling is used acquisition module 204, is used for the application data sets at described applicating category, the application of extracting coupling at the operation information of exemplary application formerly according to the user;
Use recommending module 205, be used for generating corresponding application file folder by described applicating category, corresponding application file folder is put in the application of described coupling recommended.
In specific implementation, the embodiment of the present application can also comprise as lower module:
The behavioral statistics module is used for gathering the user behavior information of submitting to after request is obtained in described application, writes in the user characteristics storehouse by user ID.
As the concrete a kind of example used 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:
The first feature extraction submodule is used for local operation behavioural information and/or online operation behavior information from described user, extracts tag along sort and the first corresponding operation frequency;
The conversion submodule is used for described tag along sort is converted to corresponding applicating category by default correlation rule; Described default correlation rule is the transformation rule of tag along sort and applicating category;
The second feature extraction submodule is used for from described user at the operation information of exemplary application formerly, extracts user operated application message and second corresponding operation frequency in the Preset Time section, comprises applicating category in the described application message;
The ordering submodule is used for calculating according to the described first operation frequency and the second operation frequency weight of each applicating category, sorts from high to low by the weight of described applicating category;
Classification is selected submodule, and preceding n the applicating category that is used for the extraction predetermined number is the applicating category of recommending to the user; Wherein, described n is the positive integer greater than 1.
In a preferred embodiment of the present application, described device can also comprise as lower module:
The application data set generation module be used for to generate the application data set of each applicating category:
Described application data set generation module is used acquisition module 204 with coupling and is connected, and specifically can comprise following submodule:
Submodule is obtained in similar application, and for the application of obtaining same applicating category, described application has tag along sort;
The similarity calculating sub module is used for determining main the application and application to be recommended in described application, and calculates application to be recommended and the main similarity of using according to the tag along sort of each application;
Quality score parameter acquiring submodule is used for obtaining described application quality grading parameters to be recommended;
Application fetches submodule to be recommended is used for extracting respectively the same main corresponding application to be recommended of using, and sorts from high to low by similarity and the quality score parameter of each application to be recommended, and extracts before the predetermined number m application to be recommended; Wherein, described m is the positive integer greater than 1;
Application data set forms submodule, is used for leading the application data set that the current application classification is formed in the application corresponding to be recommended of using and extracting.
In a preferred embodiment of the present application, described coupling is used acquisition module 204 can comprise following submodule:
Main applied statistics submodule is used for according to the user at the operation information of exemplary application formerly, and statistics is main to be used and the 3rd corresponding operation frequency, and described master is applied as the operated application of user;
Submodule is determined in application to be recommended, be used 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, with the application to be recommended that described the 3rd operation frequency is extracted some respectively as the weight of application fetches, extract the application to be recommended of satisfying first predetermined number altogether.
More preferably, described coupling is used acquisition module 204 and can also be comprised following submodule:
The main application chosen submodule, is used for obtaining the main corresponding applicating category of using, and in same applicating category, by described the 3rd operation frequency described main the application sorted, and extracts preceding k main application of predetermined number; Wherein, described k is the positive integer greater than 1;
Frequent 2 collection calculating sub module are used for main the application in twos of extracting matched, and calculate the main total degree that occurs simultaneously of using of described pairing in twos, generate frequent 2 collection;
Frequent 1 collection calculating sub module is used for calculating each main number of times that occurs separately of using, and generates frequent 1 collection;
The confidence calculations submodule is used for calculating each main degree of confidence of using according to described frequent 2 collection and frequent 1 collection, and by degree of confidence main the application is sorted;
Coupling is used and is determined submodule, the application to be recommended of satisfied first predetermined number that is used for extracting, and described main application the by the degree of confidence ordering mated, and generates the coupling of finally recommending and uses.
The embodiment of the present application not only can be applied to can also be applied to the applied environment of client-server in the applied environment of single device, perhaps further is applied in the applied environment based on cloud.
Because described device embodiment is substantially corresponding to preceding method embodiment, so not detailed part in the description of present embodiment can just not given unnecessary details at this referring to the related description in the previous embodiment.Module, submodule and unit related in the application's device embodiment and the system embodiment can be software, can be hardware, also can be the combination of software and hardware.
The application can be used in numerous general or special purpose computingasystem environment or the 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 that is 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.
At last, also need to prove, in this article, relational terms such as first and second grades only is used for an entity or operation are made a distinction with another entity or operation, and not necessarily requires or hint and have the relation of any this reality or in proper order between these entities or the operation.And, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thereby make and comprise that process, method, article or the equipment of 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.Do not having under the situation of more restrictions, the key element that is 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.
More than a kind of method and a kind of device of using automatic recommendation of using automatic recommendation that the application is provided be described in detail, used specific case herein the application's principle and embodiment are set forth, the explanation of above embodiment just is used for helping to understand the application's method and core concept thereof; Simultaneously, for one of ordinary skill in the art, according to the application's thought, the part that all can change in specific embodiments and applications, in sum, this description should not be construed as the restriction to the application.
Claims (10)
1. use the method for recommending automatically for one kind, it is characterized in that, comprising:
Receive the user and obtain request from the application of client submission, described application is obtained and is comprised user ID in the request;
From the user characteristics storehouse, extract the existing user behavior information of relative users according to described user ID, described user behavior information comprises that the user is at the operation information of exemplary application formerly, the local operation behavioural information that also comprises the user, and/or, user's online operation behavior information; Have tag along sort information in described user's local operation behavioural information and/or the online operation behavior information;
Determine to the applicating category of user's recommendation according to described user behavior information;
In the application data sets of described applicating category, extract the application of coupling at the operation information of exemplary application formerly according to the user;
Generate corresponding application file folder by described applicating category, corresponding application file folder is put in the application of described coupling recommended;
Wherein, describedly determine that according to user behavior information the step of the applicating category recommended 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; Described default correlation rule is the transformation rule of tag along sort and applicating category;
From the operation information of described user at exemplary application formerly, extract user operated application message and second corresponding operation frequency in the Preset Time section, comprise applicating category in the described application message;
Calculate the weight of each applicating category according to the described first operation frequency and the second operation frequency, sort from high to low by the weight of described applicating category;
Preceding n the applicating category that extracts predetermined number is the applicating category of recommending to the user; Wherein, described n is the positive integer greater than 1;
If extract preceding n applicating category of not enough predetermined number, then the applicating category of the applicating category that the actual access times of adding up according to high in the clouds of the network user are maximum or up-to-date setting carries out polishing as the applicating category of recommending to the user.
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, 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;
In described application, determine main the application and application to be recommended, and calculate application to be recommended and the main similarity of using according to the tag along sort of each application;
Obtain described application quality grading parameters to be recommended;
Extract the same main corresponding application to be recommended of using respectively, sort from high to low by similarity and the quality score parameter of each application to be recommended, and extract before the predetermined number m application to be recommended; Wherein, described m is the positive integer greater than 1;
The application data set of the main application corresponding to be recommended of using and extracting being formed the current application classification.
4. method as claimed in claim 3 is characterized in that, described application data sets at applicating category comprises at the step of the application of the operation information extraction coupling of exemplary application formerly according to the user:
At the operation information of exemplary application formerly, statistics is main to be used and the 3rd corresponding operation frequency according to the user, and 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, with the application to be recommended that described the 3rd operation frequency is extracted some respectively as the weight of application fetches, extract the application to be recommended of satisfying first predetermined number altogether.
5. method as claimed in claim 4 is characterized in that, described application data sets at applicating category also comprises at the step of the application of the operation information extraction coupling of exemplary application formerly according to the user:
Obtain the main corresponding applicating category of using, in same applicating category, by described the 3rd operation frequency described main the application sorted, extract preceding k main application of predetermined number; Wherein, described k is the positive integer greater than 1;
The main application in twos of extracting matched, calculate the main total degree that occurs simultaneously of using of described pairing in twos, generate frequent 2 collection;
Calculate each main number of times that occurs separately of using, generate frequent 1 collection;
Calculate each main degree of confidence of using according to described frequent 2 collection and frequent 1 collection, and by degree of confidence main the application sorted;
With the application to be recommended of satisfying first predetermined number of extracting, and described main application the by the degree of confidence ordering mated, and generates final coupling of recommending and uses.
6. use the device of recommending automatically for one kind, it is characterized in that, comprising:
The request receiver module is used for receiving the user and obtains request from the application of client submission, and described application is obtained and comprised user ID in the request;
Behavioural information extraction module formerly, be used for extracting 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 at the operation information of exemplary application formerly, the local operation behavioural information that also comprises the user, and/or, user's online operation behavior information; Have tag along sort information in described user's local operation behavioural information and/or the online operation behavior information;
The applicating category determination module is used for determining to the applicating category of user's recommendation according to described user behavior information;
Coupling is used acquisition module, is used for the application data sets at described applicating category, the application of extracting coupling at the operation information of exemplary application formerly according to the user;
Use recommending module, be used for generating corresponding application file folder by described applicating category, corresponding application file folder is put in the application of described coupling recommended;
Wherein, described applicating category determination module comprises:
The first feature extraction submodule is used for local operation behavioural information and/or online operation behavior information from described user, extracts tag along sort and the first corresponding operation frequency;
The conversion submodule is used for described tag along sort is converted to corresponding applicating category by default correlation rule; Described default correlation rule is the transformation rule of tag along sort and applicating category;
The second feature extraction submodule is used for from described user at the operation information of exemplary application formerly, extracts user operated application message and second corresponding operation frequency in the Preset Time section, comprises applicating category in the described application message;
The ordering submodule is used for calculating according to the described first operation frequency and the second operation frequency weight of each applicating category, sorts from high to low by the weight of described applicating category;
Classification is selected submodule, and preceding n the applicating category that is used for the extraction predetermined number is the applicating category of recommending to the user; Wherein, described n is the positive integer greater than 1;
Applicating category polishing module, be used for when extracting preceding n applicating category of not enough predetermined number, then the applicating category of the applicating category that the actual access times of adding up according to high in the clouds of the network user are maximum or up-to-date setting carries out polishing as the applicating category of recommending to the user.
7. device as claimed in claim 6 is characterized in that, also comprises:
The behavioral statistics module is used for gathering the user behavior information of submitting to after request is obtained in described application, writes in the user characteristics storehouse by user ID.
8. device as claimed in claim 6 is characterized in that, also comprises:
The application data set generation module be used for to generate the application data set of each applicating category: specifically comprise:
Submodule is obtained in similar application, and for the application of obtaining same applicating category, described application has tag along sort;
The similarity calculating sub module is used for determining main the application and application to be recommended in described application, and calculates application to be recommended and the main similarity of using according to the tag along sort of each application;
Quality score parameter acquiring submodule is used for obtaining described application quality grading parameters to be recommended;
Application fetches submodule to be recommended is used for extracting respectively the same main corresponding application to be recommended of using, and sorts from high to low by similarity and the quality score parameter of each application to be recommended, and extracts before the predetermined number m application to be recommended; Wherein, described m is the positive integer greater than 1;
Application data set forms submodule, is used for leading the application data set that the current application classification is formed in the application corresponding to be recommended of using and extracting.
9. device as claimed in claim 8 is characterized in that, described coupling is used acquisition module and comprised:
Main applied statistics submodule is used for according to the user at the operation information of exemplary application formerly, and statistics is main to be used and the 3rd corresponding operation frequency, and described master is applied as the operated application of user;
Submodule is determined in application to be recommended, be used 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, with the application to be recommended that described the 3rd operation frequency is extracted some respectively as the weight of application fetches, extract the application to be recommended of satisfying first predetermined number altogether.
10. device as claimed in claim 9 is characterized in that, described coupling is used acquisition module and also comprised:
The main application chosen submodule, is used for obtaining the main corresponding applicating category of using, and in same applicating category, by described the 3rd operation frequency described main the application sorted, and extracts preceding k main application of predetermined number; Wherein, described k is the positive integer greater than 1;
Frequent 2 collection calculating sub module are used for main the application in twos of extracting matched, and calculate the main total degree that occurs simultaneously of using of described pairing in twos, generate frequent 2 collection;
Frequent 1 collection calculating sub module is used for calculating each main number of times that occurs separately of using, and generates frequent 1 collection;
The confidence calculations submodule is used for calculating each main degree of confidence of using according to described frequent 2 collection and frequent 1 collection, and by degree of confidence main the application is sorted;
Coupling is used and is determined submodule, the application to be recommended of satisfied first predetermined number that is used for extracting, and described main application the by the degree of confidence ordering mated, and generates the coupling of finally recommending and uses.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110444798 CN102567511B (en) | 2011-12-27 | 2011-12-27 | Method and device for automatically recommending application |
| 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 |
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| CN 201110444798 CN102567511B (en) | 2011-12-27 | 2011-12-27 | Method and device for automatically recommending application |
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| CN201310347185.9A Division CN103455559B (en) | 2011-12-27 | 2011-12-27 | A kind of method and device applying recommendation automatically |
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| CN102567511A CN102567511A (en) | 2012-07-11 |
| CN102567511B true CN102567511B (en) | 2013-10-02 |
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| CN 201110444798 Expired - Fee Related CN102567511B (en) | 2011-12-27 | 2011-12-27 | Method and device for automatically recommending application |
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