CN102591942A - Method and device for automatic application recommendation - Google Patents
Method and device for automatic application recommendation Download PDFInfo
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- CN102591942A CN102591942A CN2011104440740A CN201110444074A CN102591942A CN 102591942 A CN102591942 A CN 102591942A CN 2011104440740 A CN2011104440740 A CN 2011104440740A CN 201110444074 A CN201110444074 A CN 201110444074A CN 102591942 A CN102591942 A CN 102591942A
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Abstract
The invention provides a method and a device for automatic application recommendation. The method includes the steps: collecting behavioral information of a user; categorizing the user behavioral information; according to the user behavioral information and categories of the same, searching matched applications from application data sets of preset corresponding categories; and generating application files corresponding to all the categories, and placing the searched applications of all the categories into the corresponding files for recommendation. The method is capable of meeting personalized needs of the user and improving recommendation efficiency and coverage rate.
Description
Technical field
The application relates to technical field of information processing, particularly relates to a kind of method and automatic recommended device of a kind of application 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, need the artificially to filter out a large amount of incoherent results simultaneously through browser; 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 along with the increase of demand, the terminal applies that people install in terminal clientsaconnect is also more and more, and 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 accomplishing client.
Although occurred the notion 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 of enterprise-level, also is not suitable for the amusement demand of domestic consumer at present.
For making the user obtain better experience, prior art has proposed the scheme that provides interested application to recommend automatically for the user, promptly through knowing user's interest place, is its recommendation initiatively, its interested application is provided.Yet, the mode that this application is recommended, main all through 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 all 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 application automatic recommended device, in order to guarantee application and the realization of said method in reality.
In order to address the above problem, the application embodiment discloses a kind of method of using automatic recommendation, specifically can comprise:
Gather user's behavioural information;
Divide the classification that said user behavior information is belonged to;
According to said user behavior information and classification thereof, search matched application in the application data sets of the corresponding classification that presets;
Generate the corresponding application file folder of each classification, corresponding application file folder is put in the application of each classification that is found recommended.
Preferably, said user's behavioural information comprises user's local operation behavioural information, and/or, user's online operation behavior information.
Preferably, the step of the classification that belonged to of said division user behavior information can comprise:
Extract the Main classification label and the corresponding operation frequency in the said user behavior information;
Convert said Main classification label into corresponding applicating category by preset correlation rule; Said preset correlation rule is the transformation rule of Main classification label and applicating category;
Add up the operation frequency of the corresponding Main classification label of each applicating category, each applicating category is sorted by the operation frequency of being added up from high to low;
Extract preceding n applicating category of predetermined number, the classification that is belonged to for active user's behavioural information; Wherein, said n is the positive integer greater than 1.
Preferably, the application of said application data sets has Main classification label and one-level subclassification label at least, and various types of other application data set is made up of the application with same Main classification label respectively;
Said according to user behavior information and classification thereof, the step of searching matched application in the application data sets of the corresponding classification that presets may further include:
The classification that the said user behavior information of foundation is belonged to is confirmed the application data set of corresponding classification;
Extract the subclassification label of said user behavior information;
In the application data sets of said corresponding classification, adopt the subclassification label of corresponding level of subclassification label and the application of said user behavior information to mate, obtain matched application and corresponding weight;
Choose preceding m the application data sets matched application of using as current classification from high to low according to said weight, wherein, said m is the positive integer greater than 1.
Preferably, said weight can comprise: the matching value between the subclassification label, perhaps, the matching value between the subclassification label and the correlation of application.
Preferably, described method can also comprise:
By the operation frequency of the corresponding Main classification label of each applicating category, the order that represents of application file folder is set;
On the desktop of subscriber equipment, represent said application file folder by the said order that represents;
In each application file folder, represent said application from high to low by the weight of using.
Preferably, described method can also comprise:
Obtain the operation information of user, the corresponding weight of using of corresponding adjustment to institute's exemplary application.
Preferably, described method can also comprise:
Obtain the operation information of user to the application file folder, what corresponding adjustment application file pressed from both sides represents in proper order.
Preferably, described method can also comprise:
User behavior information according to being gathered is set up the user characteristics storehouse;
With the operation information of user, write said user characteristics storehouse to institute's exemplary application.
The application discloses the automatic recommended device of a kind of application simultaneously, specifically can comprise:
The user behavior acquisition module is used to gather user's behavioural information;
User behavior category division module is used to divide the classification that said user behavior information is belonged to;
Coupling is used and is searched module, is used for searching matched application according to said user behavior information and classification thereof in the application data sets of the corresponding classification that presets;
Coupling is used recommending module, is used to generate the corresponding application file folder of each classification, corresponding application file folder is put in the application of each classification that is found recommended.
Preferably, said user's behavioural information comprises user's local operation behavioural information, and/or, user's online operation behavior information.
Preferably, said user behavior category division module can comprise:
Characteristic information extracts submodule, is used for extracting the Main classification label and the corresponding operation frequency of said user behavior information;
The corresponding submodule of classification is used for converting said Main classification label into corresponding applicating category by preset correlation rule; Said preset correlation rule is the transformation rule of Main classification label and applicating category;
The ordering submodule, the operation frequency that is used to add up the corresponding Main classification label of each applicating category sorts each applicating category by the operation frequency of being added up from high to low;
Sort out submodule, be used to extract preceding n applicating category of predetermined number, the classification that is belonged to for active user's behavioural information; Wherein, said n is the positive integer greater than 1.
Preferably, the application of said application data sets has Main classification label and one-level subclassification label at least, and various types of other application data set is made up of the application with same Main classification label respectively;
Said coupling application is searched module and may further include:
Application data set is confirmed submodule, is used for confirming according to the classification that said user behavior information is belonged to the application data set of corresponding classification;
The tag extraction submodule is used to extract the subclassification label of said user behavior information;
The tag match submodule is used for the application data sets in said corresponding classification, adopts the subclassification label of corresponding level of subclassification label and the application of said user behavior information to mate, and obtains matched application and corresponding weight;
Submodule is chosen in application, is used for choosing preceding m the application data sets matched application of using as current classification from high to low according to said weight, and wherein, said m is the positive integer greater than 1.
Preferably, said weight can comprise: the matching value between the subclassification label, perhaps, the matching value between the subclassification label and the correlation of application.
Preferably, described device can also comprise:
The ordering of application file folder represents module, is used for the operation frequency by the corresponding Main classification label of each applicating category, and the order that represents of application file folder is set; And on the desktop of subscriber equipment, represent said application file folder by the said order that represents;
Use ordering and represent module, be used for representing said application from high to low by the weight of using at each application file folder.
Preferably, described device can also comprise:
The weight adjusting module is used to obtain the operation information of user to institute's exemplary application, the corresponding weight of using of corresponding adjustment.
Preferably, described device can also comprise:
Application file folder order adjusting module is used to obtain the operation information of user to the application file folder, and what corresponding adjustment application file pressed from both sides represents in proper order.
Preferably, described device can also comprise:
Feature database is set up module, is used for setting up the user characteristics storehouse according to the user behavior information of being gathered;
The feature database writing module is used for the operation information to institute's exemplary application with the user, writes said user characteristics storehouse.
Compared with prior art, the application has the following advantages:
The application sorts out according to user's behavioural information; Form the application file folder of respective classes; Search matched application based on said classification in the application data sets of corresponding classification then, these application are put into the file of corresponding classification and recommend, 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 with user interface as inlet, directly on the interface or through the link on the interface through the application file clip icon to user's exemplary application obtain required application so that the user is faster easier, made things convenient for user's operation; And, can point out the user use through icon as the mode of application entrance, but before the user really selected to use, actual installation should be used corresponding configuration file to this application, 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 process flow diagram of using the method embodiment 1 of automatic recommendation of the application;
Fig. 2 is a kind of process flow diagram of using the method embodiment 2 of automatic recommendation of the application;
Fig. 3 is a kind of structured flowchart of using automatic recommended device embodiment of the application.
Embodiment
For above-mentioned purpose, the feature and advantage that make the application can be more obviously understandable, the application is done further detailed explanation below in conjunction with accompanying drawing and embodiment.
The core idea of the application embodiment is; Behavioural information according to the user is sorted out; Form the application file folder of respective classes; Search matched application based on said classification in the application data sets of corresponding classification then, these application are put into the file of corresponding classification 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:
As the concrete a kind of example used of the application embodiment, said user's behavioural information can comprise user's local operation behavioural information, and/or, user's online operation behavior information.
Said user behavior information can be gathered by the client software that is installed on the subscriber equipment, and wherein, said subscriber equipment can comprise all kinds of intelligent terminals such as computing machine, notebook computer, mobile phone, PDA, panel computer.Several kinds of 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 through browser, comprises network address and the corresponding access times of visit etc.;
Online operation behavior information as gathering through browser in the user 15 days is:
| The visit network address | Access times |
| 4939.com | 31 |
| Qiyi.com | 2 |
| Youku.com | 7 |
| 7k7k.com | 4 |
Example 2; Through being installed in the fail-safe software collection user's on the subscriber equipment local operation behavioural information; Online operation behavior information and local behavioural information as gathering through 360 net shields in the users 15 days are: open MPC and number of times thereof, open certain recreation 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 all is feasible that those skilled in the art adopt any mode to gather required user behavior information according to actual conditions, and the application embodiment need not this to limit.
The classification that step 102, the said user behavior information of division are belonged to;
In a kind of preferred embodiment of the application, said step 102 specifically can comprise following substep:
The Main classification label and the corresponding operation frequency in substep S11, the said user behavior information of extraction;
Substep S12, convert said Main classification label into corresponding applicating category by preset correlation rule; Said preset correlation rule is the transformation rule of Main classification label and applicating category;
Substep S13, the operation frequency of adding up the corresponding Main classification label of each applicating category sort each applicating category by the operation frequency of being added up from high to low;
Substep S14, extract preceding n applicating category of predetermined number, the classification that is belonged to for active user's behavioural information; Wherein, said n is the positive integer greater than 1.
In reality, can through the analysis user behavioural information, obtain the application file folder basic classification of user behavior information conforms according to the basic classification (applicating category) that the application file folder is set by the technician in advance.For example; The application file folder basic classification that is provided with in advance has 20; And through the analysis user behavioural information, find that it is unwanted for the active user that some basic classification are arranged, the classification that then can divide user behavior information and belonged to is 3 or 5 of behavioural habits before being close to the users more.For example, video, recreation, education etc.
Said user's local operation behavioural information can have label (tag) information usually with online operation behavior information; For example; For the video that the user opens at local operation, have 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 is visited, have the label informations such as king of video, film, comedy movie, comedy on the net.
From the label that said user behavior information is obtained, confirm the Main classification label, as above animation in the example or film mate with the application file folder basic classification that is provided with in advance, judge which kind of application file folder classification the Main classification label should belong in.For example, it is as shown in the table the transformation rule of Main classification label and applicating category to be set:
Use above-mentioned transformation rule, then go up Main classification label " animation " or " film " in the example, all can be exchanged into corresponding applicating category and be " video ", promptly confirm to adopt the application file folder of visual classification.
For example: the nearest 15 days net shield data of user: data11 is extracted in (1), can comprise Main classification label interest and operation frequency weight among the data11: as:
| interest | weight |
| novel-dm | 1 |
| comic-dm | 4 |
| 4399-dm | 1 |
(2) will be from the Main classification label of net shield extracting data; Be converted into the basic classification that application file presss from both sides the user interest under the taxonomic hierarchies through preset conversion rule table (yunCatToZhuoMianCat.conf), be about to the Main classification label and convert corresponding applicating category into.Can comprise in the said preset conversion rule table yunCatToZhuoMianCat.conf form: the information of Main classification label, applicating category title and applicating category id.As:
| The Main classification label | The applicating category title | Applicating category id |
| 4399-dm | Recreation | 5 |
| comic-dm | The fashion amusement | 8 |
| novel-dm | Novel | 11 |
(3) add up the operation frequency of the corresponding Main classification label of each applicating category, each applicating category is sorted by the operation frequency of being added up from high to low; Extract preceding 9 applicating categories, be the classification that active user's behavioural information is belonged to, the promptly final classification application file of showing.As:
| The Main classification label | The applicating category title | Applicating category id | weight |
| comic-dm | The fashion amusement | 8 | 4 |
| novel-dm | Novel | 11 | 1 |
| 4399-dm | Recreation | 5 | 1 |
According to this example, confirm that the classification that active user's behavioural information is belonged to is fashion amusement, novel, recreation, i.e. the application file folder of the amusement of the corresponding generation fashion of back extended meeting, novel, three kinds of classification of recreation.
In concrete the realization; If user behavior information is analyzed the classification of being divided can't reach specified quantity, as adopt example can only generate three classifications, 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 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; For example, to last example, can increase video, education, picture, music, children, these 6 applicating categories of utility again.
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 convert applicating category into according to presetting rule; Perhaps, directly extract the Main classification label as applicating category etc., the application does not limit this.
Said application (Application) is meant user's employed various services on network, like 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.Application itself can be with some labels, in the application embodiment, can classify to said label, promptly is divided into Main classification label and subclassification label, and wherein, said subclassification label can further be divided into a plurality of ranks.For example, the Main classification label is a video, and first order subclassification label is a film, and second level subclassification label is comedy movie, horrow movie or action movie etc.That is to say; The application of said application data sets has Main classification label and one-level subclassification label at least; Various types of other application data set promptly is made up of the application with same Main classification label respectively, and for example, some uses the Main classification label that all has video; Then together, form other application data set of video class with these application combination.
In a kind of preferred embodiment of application, said step 103 may further include following substep:
The classification that substep S21, the said user behavior information of foundation are belonged to is confirmed the application data set of corresponding classification;
For example, the classification that active user's behavioural information is belonged to is fashion amusement, novel, recreation, and the application data set of then confirming comprises the application data set of fashion amusement classification, promptly has the data set that the application of the Main classification label of fashion amusement is formed; The application data set of novel classification promptly has the data set that the application of the Main classification label of novel is formed; Other application data set of game class promptly has the data set that the application of the Main classification label of recreation is formed.
The subclassification label of substep S22, the said user behavior information of extraction;
As previously mentioned; Said user's local operation behavioural information can have label (tag) information usually with online operation behavior information; For example, for the video that the user opens at local operation, have 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 is visited, have the label informations such as king of video, film, comedy movie, comedy on the net.
In the label information of these user behavior information, extract the subclassification label; As above in the example; Can extract one-level subclassification label: serial, animation, film; Secondary subclassification label: illusion, risk, comedy movie, three grades of subclassification labels: the king of the fiery shadow person of bearing, bank Ben Qishi, comedy.It all is feasible that those skilled in the art divide a plurality of other subclassification labels of level according to actual conditions, and the application does not limit this.Need to prove, use present embodiment, need divide the subclassification label of one-level at least, to carry out follow-up tag match.
Substep S23, in the application data sets of said corresponding classification, adopt the subclassification label of corresponding level of subclassification label and the application of said user behavior information to mate, obtain matched application and corresponding weight; Choose preceding m the application data sets matched application of using as current classification from high to low according to said weight, wherein, said m is the positive integer greater than 1.
Because in the application data sets of certain classification, often exist thousands of to use, said substep S23 promptly passes through the application data sets institute matched application of the subclassification label of tag match algorithm computation user behavior information in corresponding classification.Concrete example as the application embodiment; Said weight can comprise: the matching value between the subclassification label; Promptly for example, the matching value of the subclassification label that the subclassification label of calculating user behavior information and the application data sets of corresponding classification are used, perhaps; The comprehensive evaluation parameter of the matching value between the subclassification label and the correlation of application, said correlation promptly refer to use every day download and user's scoring.
As concrete another example used of present embodiment, said substep S23 may further include following substep:
Substep S23-1, according to the subclassification label of user behavior information; In the application data sets retrieval character related application of said corresponding classification, said characteristic related application is the identical or all application of identical subclassification label of the subclassification label segment with user behavior information;
In the present embodiment, user's behavioural information all comprises the subclassification label, and through with the subclassification tag match of using, retrieve the characteristic related application.
The matching value of the subclassification label of substep S23-2, the subclassification label that calculates said characteristic related application and active user's behavioural information;
In concrete realization, can give certain matching value with the subclassification label of user behavior information, and these subclassification labels are divided into different groups, the matching value of subclassification label is the same on the same group.
For example, the subclassification label of active user's behavioural information is as shown in the table:
| TV play | Comedy | Love | The story of a play or opera | Bao Jianfeng | Jin Sha | Li Jichang |
Next to matching value of subclassification label distribution of each group, as above example, with these subclassification labels be divided into 4 groups and give matching value of each subclassification label after obtain:
TV play 60
Comedy 6, love 6, the story of a play or opera 6
Protect sword cutting edge of a knife or a sword 2, Jin Sha 2
Li Ji prosperous 1
Next; The subclassification label of the characteristic related application of finding and the subclassification label of active user's behavioural information are compared; Calculate the matching value of each characteristic related application by subclassification tag hit situation; Wherein, hit rate (tag that hits group number/total tag group) with hit matching value ratio (the weights sum that hits/weights are total) weight separately and can adjust according to business rule, summation is 100.Wherein tag promptly refers to the subclassification label.Such as video is 50: 50, and the matching value computing formula of the characteristic related application of video class is:
Weight=(the tag group number that hits/total tag group) * 50+ (the matching value sum that hits/matching value sum) * 50;
Need to prove have one to hit and promptly can be regarded as this group and hit in the tag group, matching value can round up.
As above routine, the tag that searches certain characteristic related application is:
| TV play | Other | Love | The story of a play or opera | Liu Dehua | Jin Sha | Li An | Other | 2011 | The continent |
The subclassification label contrast of itself and active user's behavioural information can be found that the tag that hits has TV play, love, the story of a play or opera; Jin Sha has 3 groups (classification, type; Act the leading role) hit, then the matching value of this application is exactly weight=3/4*50+74/83*50=68 (67.17 round up 68).
Substep S23-3, the characteristic related application is sorted according to matching value is descending;
After having calculated the matching value of each characteristic related application, can sort from big to small by matching value.Certain is several for each application of taking out is unlikely to focus on; Application after letting those relatively lean on also can recommendedly be arrived; Can respectively from several matching values interval of reserving in advance, choose some matching values and drop on the recommendation that should be used in this interval, for example from mate interval 100-88, select 3; From 87-73, select 2, from 72-16, select 1.Wherein, in same interval, what matching value was high preferentially is selected.If use not enough quantity in certain is interval, then choose and supply from the low interval of closing on.
Substep S23-4, the said ordering of foundation, the preset number of extraction, matching value meets the characteristic related application of a plurality of pre-set interval respectively, as matched application.
In concrete the realization, when the matching value of a plurality of characteristic related application equated, said substep S23 can also comprise:
Substep S23-5, calculate the matching value of said characteristic related application and the subclassification label of user behavior information, and the correlation of the subclassification label of equal each characteristic related application of matching value and active user's behavioural information.
Under the same situation of the matching value of a plurality of characteristic related application, the correlation of the characteristic related application of needs calculating identical match value and the subclassification label of active user's behavioural information.
For example, suppose the application A relevant with the subclassification label of active user's behavioural information, Application of B, the matching value of using C is identical, and computation process can be following:
The download of download+C of download+B of total download=A;
The scoring of scoring+C of scoring+B of overall score=A;
The correlativity of using A is: scoring/overall score * 40 of the download of A.assoc=A/total download * 60+A;
The correlativity of Application of B is: scoring/overall score * 40 of the download of B.assoc=B/total download * 60+B;
The correlativity of using C is: scoring/overall score * 40 of the download of C.assoc=C/total download * 60+C.
Substep S22-5, the characteristic related application is sorted according to matching value from big to small, wherein, the characteristic related application that matching value equates sorts according to correlation is descending;
Substep S22-6, the said ordering of foundation, the preset number of extraction, matching value meets the characteristic related application of a plurality of pre-set interval respectively, as exemplary application.
For the situation of the characteristic related application that has the identical match value, calculated matching value and correlativity after, all characteristic related application are sorted earlier according to the matching value size, for the application of identical match value, sort according to the correlation size.Can respectively from several matching values interval of reserving in advance, choose some matching values and drop on the recommendation that should be used in this interval then, in same interval, what matching value was high preferentially is selected, and it is high that identical weights are then preferentially chosen the degree of correlation.
For example, searched 10 characteristic related application, corresponding matching value and correlation are following:
| A1 | A2 | B | C | D1 | D2 | E | F | G | H | |
| Matching value | 93 | 93 | 89 | 83 | 57 | 57 | 50 | 49 | 32 | 23 |
| Correlation | 239 | 234 | 2334 | 455 |
Pre-set interval and corresponding preset number are: 3 [100-88], 2 [87-73], 1 [72-16]; Can from [87-73], choose C and D1 and (, then from [72-16] interval, choose the highest supplying from [100-88] from choosing A1, A2 and B according to the order in the table because this interval has only an application; D1 is consistent with the D2 matching value, but the D1 correlation is greater than D2, so choose D1); From 72-16, choose E, totally 6 application are as exemplary application.
In reality; If according to said user behavior information and classification thereof; The coupling that finds in the application data sets of corresponding classification is used and is not satisfied predetermined number; The application data sets access times application maximum and/or up-to-date warehouse-in that can extract respective classes for example, is extracted 20 application the most popular as exemplary application in the application data sets of current classification as exemplary application.
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, the subclassification label through calculating user behavior information and the similarity of the subclassification label of respective classes application data sets application etc., the application need not this to limit.
Need to prove; In the application embodiment; Said Main classification label and subclassification label can mark through person skilled or user voluntarily; Also can adopt the computing machine clustering technique, obtain, can also gather the descriptor of corresponding software or application from network (for example official) through semantic or key word analysis to the webpage literal.
Use the application embodiment, category is generated the application file folder, under the respective classes, promptly in the application file folder of corresponding classification, recommend with the application of user behavior information matches to the user, thus the resource that helps saving subscriber equipment.
With reference to Fig. 2, 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:
The classification that step 202, the said user behavior information of division are belonged to;
In concrete the realization, the application file folder for recommending the user can represent in the different split screens of desktop, preferably, can also be according to the height and the width of user's split screen, the number of the application file folder of confirming to recommend in each split screen.Use the application embodiment, the order that represents of said application file folder is that the operation frequency according to the corresponding Main classification label of each applicating category is provided with from high to low, so the application file folder is that matching degree according to user interest represents to the user from high to low; And the application in the application file folder is also sorted by weight, promptly also is that the matching degree according to user interest represents to the user from high to low, thereby operation that can more convenient user makes the user obtain better experience.
In a kind of preferred embodiment of the application, can also comprise the steps:
Obtain the operation information of user, the corresponding weight of using of corresponding adjustment to institute's exemplary application.
When after user's exemplary application; The user may open this application, checks details, also may further the application of recommending be added in the use of oneself; In this case; Can also improve the weight of the operated application of user according to the behavioural information of user, thereby change the ordering of using in the application file folder to exemplary application.
In concrete the realization, can also for example click the operation frequency of application file folder through obtaining the operation information of user, according to the order that represents of the corresponding adjustment application file folder of the frequency of each application file double-layered quilt operation to the application file folder according to the user.
In concrete the realization, can unify in the user interface of terminal desktop to show and press from both sides corresponding icon that each icon is represented an application file folder, through the mode of icon conduct with application entrance with a plurality of application files.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 " is behind the icon of user's click " video " application file folder; Get into the subwindow of this application file folder, in subwindow, showing has a plurality of application icons such as TV play, film, animation, variety.Can point out the user use through icon as the mode of application entrance to this application; 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 said 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 through configuration file, and this has just prevented rogue program the distorting reference address of end side.
And; The network side central server can be through 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 through the address information after upgrading with the mutual acquisition of content server; And send over through 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 of acquisition and the corresponding application of said icon.For example, do not obtain configuration file before, icon can be a black and white, or dark-coloured, and after acquisition, can become colour or light tone.
What also need explain is that the application file clip icon of in the end side user interface, showing can be one or more, can confirm according to different displaying rules.For example, when using an icon, this icon can be used as the unified inlet 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 at this inlet icon place to point out.
In a kind of preferred embodiment of the application, can also comprise the steps:
User behavior information according to being gathered is set up the user characteristics storehouse;
With the operation information of user, write said user characteristics storehouse to institute's exemplary application.
Through setting up the user characteristics storehouse; Then can user behavior information be unified in server end or high in the clouds is handled; In this embodiment; Can in the user characteristics storehouse, work as inferior operation behavior information by recording user, and confirm and to press from both sides and application corresponding to the application file that the user recommends according to the previous operation behavior information in user characteristics storehouse.Need to prove that in the present embodiment, said user behavior information comprises that also the user is directed against the operation information of institute's exemplary application.
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 does not receive 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. 3, show a kind of structured flowchart of using automatic recommended device embodiment of the application, specifically can comprise like lower module:
User behavior acquisition module 301 is used to gather user's behavioural information;
User behavior category division module 302 is used to divide the classification that said user behavior information is belonged to;
Coupling is used and is searched module 303, is used for searching matched application according to said user behavior information and classification thereof in the application data sets of the corresponding classification that presets;
Coupling is used recommending module 304, is used to generate the corresponding application file folder of each classification, corresponding application file folder is put in the application of each classification that is found recommended.
In concrete the realization, said user's behavioural information can comprise user's local operation behavioural information, and/or, user's online operation behavior information.
In a kind of preferred embodiment of the application, said user behavior category division module 302 can comprise following submodule:
Characteristic information extracts submodule, is used for extracting the Main classification label and the corresponding operation frequency of said user behavior information;
The corresponding submodule of classification is used for converting said Main classification label into corresponding applicating category by preset correlation rule; Said preset correlation rule is the transformation rule of Main classification label and applicating category;
The ordering submodule, the operation frequency that is used to add up the corresponding Main classification label of each applicating category sorts each applicating category by the operation frequency of being added up from high to low;
Sort out submodule, be used to extract preceding n applicating category of predetermined number, the classification that is belonged to for active user's behavioural information; Wherein, said n is the positive integer greater than 1.
As the concrete a kind of example used of the application embodiment, the application of said application data sets has Main classification label and one-level subclassification label at least, and various types of other application data set is made up of the application with same Main classification label respectively; In this case, said coupling application is searched module 303 and may further include following substep:
Application data set is confirmed submodule, is used for confirming according to the classification that said user behavior information is belonged to the application data set of corresponding classification;
The tag extraction submodule is used to extract the subclassification label of said user behavior information;
The tag match submodule is used for the application data sets in said corresponding classification, adopts the subclassification label of corresponding level of subclassification label and the application of said user behavior information to mate, and obtains matched application and corresponding weight;
Submodule is chosen in application, is used for choosing preceding m the application data sets matched application of using as current classification from high to low according to said weight, and wherein, said m is the positive integer greater than 1.
Preferably, said weight can comprise: the matching value between the subclassification label, perhaps, the matching value between the subclassification label and the correlation of application.
In a kind of preferred embodiment of the application, said device embodiment can also comprise like lower module:
The ordering of application file folder represents module, is used for the operation frequency by the corresponding Main classification label of each applicating category, and the order that represents of application file folder is set; And on the desktop of subscriber equipment, represent said application file folder by the said order that represents;
Use ordering and represent module, be used for representing said application from high to low by the weight of using at each application file folder.
More preferably, said device embodiment can also comprise like lower module:
The weight adjusting module is used to obtain the operation information of user to institute's exemplary application, the corresponding weight of using of corresponding adjustment.
More preferably, said device embodiment can also comprise like lower module:
Application file folder order adjusting module is used to obtain the operation information of user to the application file folder, and what corresponding adjustment application file pressed from both sides represents in proper order.
More preferably, said device embodiment can also comprise like lower module:
Feature database is set up module, is used for setting up the user characteristics storehouse according to the user behavior information of being gathered;
The feature database writing module is used for the operation information to institute's exemplary application with the user, writes said user characteristics storehouse.
The application embodiment 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 said device embodiment is basically 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.What each embodiment in this instructions stressed all is and the difference of other embodiment that identical similar part is mutually referring to getting final product between each embodiment.
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 DCE of above any system or equipment or the like.
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, program, object, assembly, data structure or the like.Also can in DCE, put into practice the application, in these DCEs, by through communication network connected teleprocessing equipment execute the task.In DCE, program module can be arranged in this locality and the remote computer storage medium that comprises memory device.
More than a kind of method and automatic recommended device of a kind of application of using automatic recommendation that the application provided carried out detailed introduction; Used concrete example among this paper the application's principle and embodiment are set forth, the explanation of above embodiment just is used to help 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 on embodiment and range of application, all can change, in sum, this description should not be construed as the restriction to the application.
Claims (18)
1. use the method for recommending automatically for one kind, it is characterized in that, comprising:
Gather user's behavioural information;
Divide the classification that said user behavior information is belonged to;
According to said user behavior information and classification thereof, search matched application in the application data sets of the corresponding classification that presets;
Generate the corresponding application file folder of each classification, corresponding application file folder is put in the application of each classification that is found recommended.
2. the method for claim 1 is characterized in that, said user's behavioural information comprises user's local operation behavioural information, and/or, user's online operation behavior information.
3. the method for claim 1 is characterized in that, the step of the classification that said division user behavior information is belonged to comprises:
Extract the Main classification label and the corresponding operation frequency in the said user behavior information;
Convert said Main classification label into corresponding applicating category by preset correlation rule; Said preset correlation rule is the transformation rule of Main classification label and applicating category;
Add up the operation frequency of the corresponding Main classification label of each applicating category, each applicating category is sorted by the operation frequency of being added up from high to low;
Extract preceding n applicating category of predetermined number, the classification that is belonged to for active user's behavioural information; Wherein, said n is the positive integer greater than 1.
4. method as claimed in claim 3 is characterized in that, the application of said application data sets has Main classification label and one-level subclassification label at least, and various types of other application data set is made up of the application with same Main classification label respectively;
Said according to user behavior information and classification thereof, the step of searching matched application in the application data sets of the corresponding classification that presets further comprises:
The classification that the said user behavior information of foundation is belonged to is confirmed the application data set of corresponding classification;
Extract the subclassification label of said user behavior information;
In the application data sets of said corresponding classification, adopt the subclassification label of corresponding level of subclassification label and the application of said user behavior information to mate, obtain matched application and corresponding weight;
Choose preceding m the application data sets matched application of using as current classification from high to low according to said weight, wherein, said m is the positive integer greater than 1.
5. method as claimed in claim 4 is characterized in that, said weight comprises: the matching value between the subclassification label, perhaps, the matching value between the subclassification label and the correlation of application.
6. method as claimed in claim 3 is characterized in that, also comprises:
By the operation frequency of the corresponding Main classification label of each applicating category, the order that represents of application file folder is set;
On the desktop of subscriber equipment, represent said application file folder by the said order that represents;
In each application file folder, represent said application from high to low by the weight of using.
7. like claim 4 or 5 or 6 described methods, it is characterized in that, also comprise:
Obtain the operation information of user, the corresponding weight of using of corresponding adjustment to institute's exemplary application.
8. like claim 4 or 5 or 6 described methods, it is characterized in that, also comprise:
Obtain the operation information of user to the application file folder, what corresponding adjustment application file pressed from both sides represents in proper order.
9. the method for claim 1 is characterized in that, also comprises:
User behavior information according to being gathered is set up the user characteristics storehouse;
With the operation information of user, write said user characteristics storehouse to institute's exemplary application.
10. use automatic recommended device for one kind, it is characterized in that, comprising:
The user behavior acquisition module is used to gather user's behavioural information;
User behavior category division module is used to divide the classification that said user behavior information is belonged to;
Coupling is used and is searched module, is used for searching matched application according to said user behavior information and classification thereof in the application data sets of the corresponding classification that presets;
Coupling is used recommending module, is used to generate the corresponding application file folder of each classification, corresponding application file folder is put in the application of each classification that is found recommended.
11. device as claimed in claim 10 is characterized in that, said user's behavioural information comprises user's local operation behavioural information, and/or, user's online operation behavior information.
12. device as claimed in claim 10 is characterized in that, said user behavior category division module comprises:
Characteristic information extracts submodule, is used for extracting the Main classification label and the corresponding operation frequency of said user behavior information;
The corresponding submodule of classification is used for converting said Main classification label into corresponding applicating category by preset correlation rule; Said preset correlation rule is the transformation rule of Main classification label and applicating category;
The ordering submodule, the operation frequency that is used to add up the corresponding Main classification label of each applicating category sorts each applicating category by the operation frequency of being added up from high to low;
Sort out submodule, be used to extract preceding n applicating category of predetermined number, the classification that is belonged to for active user's behavioural information; Wherein, said n is the positive integer greater than 1.
13. device as claimed in claim 12 is characterized in that, the application of said application data sets has Main classification label and one-level subclassification label at least, and various types of other application data set is made up of the application with same Main classification label respectively;
Said coupling application is searched module and is further comprised:
Application data set is confirmed submodule, is used for confirming according to the classification that said user behavior information is belonged to the application data set of corresponding classification;
The tag extraction submodule is used to extract the subclassification label of said user behavior information;
The tag match submodule is used for the application data sets in said corresponding classification, adopts the subclassification label of corresponding level of subclassification label and the application of said user behavior information to mate, and obtains matched application and corresponding weight;
Submodule is chosen in application, is used for choosing preceding m the application data sets matched application of using as current classification from high to low according to said weight, and wherein, said m is the positive integer greater than 1.
14. device as claimed in claim 13 is characterized in that, said weight comprises: the matching value between the subclassification label, perhaps, the matching value between the subclassification label and the correlation of application.
15. device as claimed in claim 12 is characterized in that, also comprises:
The ordering of application file folder represents module, is used for the operation frequency by the corresponding Main classification label of each applicating category, and the order that represents of application file folder is set; And on the desktop of subscriber equipment, represent said application file folder by the said order that represents;
Use ordering and represent module, be used for representing said application from high to low by the weight of using at each application file folder.
16. like claim 13 or 14 or 15 described devices, it is characterized in that, also comprise:
The weight adjusting module is used to obtain the operation information of user to institute's exemplary application, the corresponding weight of using of corresponding adjustment.
17. like claim 13 or 14 or 15 described devices, it is characterized in that, also comprise:
Application file folder order adjusting module is used to obtain the operation information of user to the application file folder, and what corresponding adjustment application file pressed from both sides represents in proper order.
18. device as claimed in claim 10 is characterized in that, also comprises:
Feature database is set up module, is used for setting up the user characteristics storehouse according to the user behavior information of being gathered;
The feature database writing module is used for the operation information to institute's exemplary application with the user, writes said user characteristics storehouse.
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| CN2011104440740A CN102591942B (en) | 2011-12-27 | 2011-12-27 | Method and device for automatic application recommendation |
| CN201310462445.7A CN103744849B (en) | 2011-12-27 | 2011-12-27 | Method and device for automatic recommendation application |
| CN201310462769.0A CN103488788B (en) | 2011-12-27 | 2011-12-27 | It is a kind of to apply the method and device recommended automatically |
Applications Claiming Priority (1)
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| CN2011104440740A CN102591942B (en) | 2011-12-27 | 2011-12-27 | Method and device for automatic application recommendation |
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| CN201310462769.0A Division CN103488788B (en) | 2011-12-27 | 2011-12-27 | It is a kind of to apply the method and device recommended automatically |
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| CN102591942B CN102591942B (en) | 2013-11-13 |
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| CN2011104440740A Expired - Fee Related CN102591942B (en) | 2011-12-27 | 2011-12-27 | Method and device for automatic application recommendation |
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