CN106294752A - The application method of special recommendation, device and server - Google Patents
The application method of special recommendation, device and server Download PDFInfo
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Abstract
The embodiment of the present invention provides one application special recommendation method, device and server, and described method includes: according to the list of application of multiple users, calculates user's prediction interest-degree to each application in all list of applications；According to the degree of association between described user prediction interest-degree and each application described and each application special topic to each application in all list of applications, calculate the described user prediction interest-degree to each application special topic；According to the described user prediction interest-degree to each application special topic, determine application special topic to be recommended.Default special topic can be carried out Auto-matching with user by described method, different special topics can be recommended for different users, improve Consumer's Experience, and improve the work efficiency of operation personnel, and then the human cost in platform that whole application special recommendation services or application shop is reduced, overall operation efficiency is improved.
Description
Technical field
The present invention relates to computer application field, in particular to one application special recommendation method, device and service
Device.
Background technology
Along with developing rapidly of mobile terminal technology and network technology, increasing user selects at mobile phone or flat board electricity
Downloading application software on the mobile terminals such as brain, various aim at the application that mobile phone users makes and obtains platform, such as, apply business
Also arise at the historic moment in shop.
During the operation of application shop, it will usually select some relevant application packages and become a special topic, mix a series of
Problem and pattern, be pushed to user, download with the application facilitating user to select in relevant application oneself to like.More conventional
Be the homepage being directly illustrated in mobile phone assistant, abundant page elements, increase Consumer's Experience.But recommend currently to each user
Special topic be just as, not there is specific aim, Consumer's Experience is poor.
Summary of the invention
In view of this, the purpose of the embodiment of the present invention is to provide a kind of application special recommendation method, device and server,
To solve the problems referred to above.
To achieve these goals, the technical scheme that the embodiment of the present invention uses is as follows:
First aspect, embodiments provides a kind of application special recommendation method, and described method includes: according to multiple
The list of application of user, calculates user's prediction interest-degree to each application in all list of applications；According to described user couple
Degree of association between prediction interest-degree and each application described and each application special topic of each application in all list of applications,
Calculate the described user prediction interest-degree to each application special topic；According to the described user prediction interest-degree to each application special topic, really
Fixed application special topic to be recommended.
Second aspect, embodiments provides a kind of application special recommendation device, and described device includes: first calculates
Module, for the list of application according to multiple users, calculates user's prediction interest to each application in all list of applications
Degree；Second computing module, for the prediction interest-degree applied each in all list of applications according to described user and institute
State the degree of association between each application and each application special topic, calculate the described user prediction interest-degree to each application special topic；Determine
Module, for applying thematic prediction interest-degree according to described user to each, determines application special topic to be recommended.
The third aspect, embodiments provides a kind of server, and described server includes memorizer and processor, institute
State memorizer and be couple to described processor, described memory store instruction, make institute when executed by the processor
Stating processor and perform following operation: according to the list of application of multiple users, calculating user should to each in all list of applications
Prediction interest-degree；According to described user in all list of applications each application prediction interest-degree and described each
Degree of association between application and each application special topic, calculates the described user prediction interest-degree to each application special topic；According to described use
The family prediction interest-degree to each application special topic, determines application special topic to be recommended.
Compared with prior art, a kind of application special recommendation method, device and server, the root that the embodiment of the present invention provides
According in the described user to be recommended list of application to multiple users each application prediction interest-degree and described each application with
Preset the degree of association between special topic, calculate the described user to be recommended prediction interest-degree to described default special topic, and according to described
Prediction interest-degree recommends the application special topic being suitable for user, in this way, can be carried out certainly with user by default special topic
Dynamic coupling, it is possible to recommend different special topics for different users, improves Consumer's Experience, and pushes away for improving application special topic
For recommending platform or the application shop of service, go to judge to which user recommends which special topic owing to need not operation personnel,
Improve the work efficiency of operation personnel, and then make the platform that whole application special recommendation services or the manpower applying shop
Cost is reduced, and overall operation efficiency is improved.
For making the above-mentioned purpose of the present invention, feature and advantage to become apparent, preferred embodiment cited below particularly, and coordinate
Appended accompanying drawing, is described in detail below.
Accompanying drawing explanation
In order to be illustrated more clearly that the technical scheme of the embodiment of the present invention, below by embodiment required use attached
Figure is briefly described, it will be appreciated that the following drawings illustrate only certain embodiments of the present invention, and it is right to be therefore not construed as
The restriction of scope, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to according to this
A little accompanying drawings obtain other relevant accompanying drawings.
Fig. 1 is the schematic diagram that the user terminal that the embodiment of the present invention provides interacts with server.
Fig. 2 is the structural representation of the server that the embodiment of the present invention provides.
Fig. 3 is a kind of flow chart applying special recommendation method that the embodiment of the present invention provides.
Fig. 4 is that a kind of of embodiment of the present invention offer applies the detail flowchart of step S400 in special recommendation method.
Fig. 5 is that a kind of of embodiment of the present invention offer applies the detail flowchart of step S410 in special recommendation method.
Fig. 6 is that a kind of of embodiment of the present invention offer applies the detail flowchart of step S420 in special recommendation method.
Fig. 7 is that a kind of of embodiment of the present invention offer applies the detail flowchart of step S430 in special recommendation method.
Fig. 8 is that a kind of of embodiment of the present invention offer applies the detailed flow chart of step S600 in special recommendation method.
Fig. 9 is a kind of structured flowchart applying special recommendation device that the embodiment of the present invention provides.
Figure 10 is that a kind of of embodiment of the present invention offer applies the structural frames of the first computing module 710 in special recommendation device
Figure.
Figure 11 is that a kind of of embodiment of the present invention offer applies the structural frames of the first processing module 711 in special recommendation device
Figure.
Detailed description of the invention
Below in conjunction with accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Ground describes, it is clear that described embodiment is only a part of embodiment of the present invention rather than whole embodiments.Generally exist
Can arrange and design with various different configurations with the assembly of the embodiment of the present invention that illustrates described in accompanying drawing herein.Cause
This, be not intended to limit claimed invention to the detailed description of the embodiments of the invention provided in the accompanying drawings below
Scope, but it is merely representative of the selected embodiment of the present invention.Based on embodiments of the invention, those skilled in the art are not doing
The every other embodiment obtained on the premise of going out creative work, broadly falls into the scope of protection of the invention.
It should also be noted that similar label and letter represent similar terms, therefore, the most a certain Xiang Yi in following accompanying drawing
Individual accompanying drawing is defined, then need not it be defined further and explains in accompanying drawing subsequently.Meanwhile, the present invention's
In description, term " first ", " second " etc. are only used for distinguishing and describe, and it is not intended that indicate or hint relative importance.
Fig. 1 shows the schematic diagram that the server 200 that the embodiment of the present invention provides interacts with user terminal 100.Institute
State server 200 to be communicatively coupled with one or more user terminals 100 by network 300, to carry out data communication or friendship
Mutually.Described server 200 can be the webserver, database server etc..Described user terminal 100 can be PC
(personal computer, PC), panel computer, smart mobile phone, personal digital assistant (personal digital
Assistant, PDA), mobile unit, wearable device etc..
As in figure 2 it is shown, be the block diagram of described server 200.Described server 200 includes memorizer 201, processes
Device 202 and mixed-media network modules mixed-media 203.
Memorizer 201 can be used for storing software program and module, such as the application special recommendation side in the embodiment of the present invention
Method and programmed instruction/module corresponding to device, processor 202 by operation be stored in the software program in memorizer 201 and
Module, thus perform the application of various function and data process, i.e. realize the application special recommendation method in the embodiment of the present invention.
Memorizer 201 can include high speed random access memory, may also include nonvolatile memory, as one or more magnetic storage fills
Put, flash memory or other non-volatile solid state memories.Further, the software program in above-mentioned memorizer 201 and module
May also include that operating system 221 and service module 222.Wherein operating system 221, can be such as LINUX, UNIX,
WINDOWS, it can include various for managing system task (such as memory management, storage device control, power management etc.)
Component software and/or driving, and can communication mutual with various hardware or component software, thus provide the operation of other component softwares
Environment.On the basis of service module 222 operates in operating system 221, and by the network service of operating system 221 monitor from
The request of network, completes corresponding data according to request and processes, and return result to client.It is to say, service mould
Block 222 is for providing network service to client.
Mixed-media network modules mixed-media 203 is used for receiving and sending network signal.Above-mentioned network signal can include wireless signal or have
Line signal.
Be appreciated that the structure shown in Fig. 2 be only signal, described server 200 may also include more more than shown in Fig. 2 or
The assembly that person is less, or there is the configuration different from shown in Fig. 2.Each assembly shown in Fig. 2 can use hardware, software or
A combination thereof realizes.It addition, the server in the embodiment of the present invention can also include the server of multiple concrete difference in functionality.
In the embodiment of the present invention, being provided with client in user terminal 100, this client can be that third-party application is soft
Part, such as, apply shop, holds corresponding with server (Server), jointly follows same set of data protocol so that service end with
Client can parse mutually the data of the other side, provides the user application special recommendation service.
Fig. 3 shows the flow chart applying special recommendation method that the embodiment of the present invention provides, and refers to Fig. 3, this enforcement
What example described is the handling process of server, and described method includes:
Step S400, according to the list of application of multiple users, calculates user to each application in all list of applications
Prediction interest-degree.
Wherein, server can obtain the list of application of multiple user respectively from multiple user terminals, it is also possible to user
When application is downloaded/installed to server, server is by log recording and stores the application that user downloads/installs, by obtaining
Described user journal, server can obtain the list of application of multiple user.
Described list of application can include user installed list of application, nearest n days download list of application, nearest n days clear
Look at list of application, nearest n days update list of application, nearest n days search list of application, nearest n days start list of application, wait application
List.
As a kind of embodiment, system pre-installation application can be installed the list of application from the plurality of user
After removal, it is thus achieved that list of application.The user so the calculated prediction interest-degree to each application in all list of applications,
More conform to the personal habits of user.
The embodiment of step S400 has multiple, and one is described below, but it is understood that, it is not limited to this.
Referring to Fig. 4, step S400 may include that
Step S410, calculate each application in the list of application of multiple user to other relevant between any one application
Degree.
The embodiment of step S410 has multiple.As a kind of embodiment, can be by judging whether two application belong to
In a classification, if said two application belongs to a classification, the degree of association that said two is applied can be placed in 1, otherwise,
It is set to 0.
Server can pre-set the classification chart about application, classification chart has the class name presetting classification.Service
Whether the Euclidean distance of cryptographic Hash and the cryptographic Hash of the class name in classification chart that device calculates Apply Names is less than predetermined threshold value, as
Fruit less than described predetermined threshold value, then may determine that described application belongs to described default classification.Judge two the most by the way
Classification belonging to individual application, if said two application belongs to a classification, can be placed in the degree of association that said two is applied
1, otherwise, it is set to 0.In this way, each application in multiple user's list of application it is calculated successively any one with other
Degree of association between individual application.
As another embodiment, referring to Fig. 5, step S410 may include that
Step S411, according to the application in the list of application of the plurality of user, forms co-occurrence matrix, described co-occurrence matrix
The initial value of middle matrix element is zero.
Co-occurrence matrix applies, for characterizing any two, the number of times simultaneously occurred in the list of application of same user.
For example, it is assumed that the application in the list of application of the plurality of user is as shown in table 1 below:
Table 1
| User | List of application is applied |
| 1 | A, b, d |
| 2 | B, c |
| 3 | C, d |
| 4 | B, c, d |
| 5 | A, d |
The co-occurrence matrix then constituted is as follows:
| a | b | c | d | |
| a | 0 | 0 | 0 | 0 |
| b | 0 | 0 | 0 | 0 |
| c | 0 | 0 | 0 | 0 |
| d | 0 | 0 | 0 | 0 |
Step S412, travels through the list of application of the plurality of user successively, whenever two application are grasped by a user simultaneously
When making, the value of the matrix element that said two application is corresponding adds one, until having traveled through the list of application of the plurality of user, is formed
New co-occurrence matrix.
As shown in table 1:
For user 1:
(1) application a and application b occurs simultaneously, and the value of therefore corresponding with application b for application a matrix element adds 1, now altogether
Existing matrix is as follows:
| a | b | c | d | |
| a | 0 | 1 | 0 | 0 |
| b | 1 | 0 | 0 | 0 |
| c | 0 | 0 | 0 | 0 |
| d | 0 | 0 | 0 | 0 |
(2) application a and application d occurs simultaneously, and the value of therefore corresponding with application d for application a matrix element adds 1, now altogether
Existing matrix is as follows:
| a | b | c | d | |
| a | 0 | 1 | 0 | 1 |
| b | 1 | 0 | 0 | 0 |
| c | 0 | 0 | 0 | 0 |
| d | 1 | 0 | 0 | 0 |
(3) application b and application d occurs simultaneously, and the value of therefore corresponding with application d for application b matrix element adds 1, now altogether
Existing matrix is as follows:
| a | b | c | d | |
| a | 0 | 1 | 0 | 1 |
| b | 1 | 0 | 0 | 1 |
| c | 0 | 0 | 0 | 0 |
| d | 1 | 1 | 0 | 0 |
The like, each user list is performed aforesaid operations, until having traveled through the list of application of the plurality of user,
Form new co-occurrence matrix, as follows:
| a | b | c | d | |
| a | 0 | 1 | 0 | 2 |
| b | 1 | 0 | 2 | 0 |
| c | 0 | 2 | 0 | 2 |
| d | 2 | 1 | 2 | 0 |
Step S413, according to corresponding two of the value of the matrix element of described new co-occurrence matrix and each matrix element
The number of users that application is respectively corresponding, calculates in the plurality of user's list of application between each application and other any one application
Degree of association.
Wherein, the number of users of two application correspondence respectively that each matrix element is corresponding can obtain answering of multiple users
Obtain with carrying out statistics after list.
Such as: if the list of application of the user got 1 is for { a, b, c}, the list of application of user 2 is that { a, b}, user 3
List of application be that { b, c}, then application number of users corresponding to a is 2, and number of users corresponding for application b is 3, the user that application c is corresponding
Number is 2.
As a kind of embodiment, can be calculated between each application and other any one application by following formula
Degree of association:
Wherein, (i, j) represents in co-occurrence matrix the element value of the i-th row jth row to C, and N (i) represents in described co-occurrence matrix i-th
The number of users of the application i corresponding to matrix element of row jth row, N (j) represents the matrix element that in described co-occurrence matrix, the i-th row jth arranges
The number of users of the application j that element is corresponding.
Therefore, any one application of each application and other in the plurality of user's list of application calculated according to upper example
Between degree of association as shown in table 2 below:
Table 2
| Application | Application | Degree of association |
| a | b | 0.41 |
| a | d | 0.71 |
| b | a | 0.41 |
| b | c | 0.67 |
| c | b | 0.67 |
| c | d | 0.58 |
| d | a | 0.71 |
| d | b | 0.29 |
| d | c | 0.58 |
Step S420, calculates the interest-degree of each application that user is applied in list.
Implement as one, number of operations and described of each application can be applied in list according to described user
The default weight that operation is corresponding, calculates described user respectively and is applied in list the interest-degree of each application.
Referring to Fig. 6, step S420 may include that
Step S421, using any one application in described user's list of application as current application.
Wherein, the list of application of described user can include that user has installed list of application, within nearest n days, downloaded application row
Table, browse application list in nearest n days, renewal list of application, search list of application, startup in nearest n days in nearest n days in nearest n days are answered
With list, totally six class list of applications.
Step S422, operates corresponding first to the number of starts of current application with startup by described user and presets weight
Product, download time corresponding with down operation second preset that the product of weight, searching times are corresponding with search operation the 3rd
The product of default weight, installation number of times and installation operate corresponding the 4th and preset the product of weight, update times and renewal operation
The 6th product presetting weight that corresponding the 5th presets the product of weight, number of visits is corresponding with browse operation is sued for peace, it is thus achieved that
The described user interest-degree to current application.
Can calculate the described user interest-degree to current application j according to the following formula:
Rij=t1×startij+t2×downij+t3×searchij+t4×viewij+t5×installij+t6×
updateij
Wherein, startijIt is the described user number of times that starts current application j, downijIt is that described user downloads current application
The number of times of j, searchijIt is the described user number of times of searching for current application j, viewijIt is that described user browses current application j
Number of times, updateijIt is the described user number of times that updates current application j, installijIt it is described user installation current application j
Number of times (is installed as 1, be otherwise 0), t1Weight, t is preset for starting the first of operation correspondence2For corresponding second pre-of down operation
If weight, t3For search operation corresponding the 3rd preset weight, t6Weight is preset, t for updating the 5th of operation correspondence the4For browsing
The 6th of operation correspondence presets weight, t5Presetting weight for installing the 4th of operation correspondence the, described default weight can be
Constant.
Step S423, using another application in the list of application of described user as current application, until described user
The interest-degree of the application being applied in list all calculates complete.
Step S430, according to each application in the plurality of user's list of application with other any one application between phase
Pass degree and described user are applied in list the interest-degree of each application, calculate described user in all list of applications
The prediction interest-degree of each application.
The embodiment of step S430 has multiple, and one is described below, it is to be understood that be not limited thereto.
Referring to Fig. 7, step S430 may include that
Step S431, using any one application in the plurality of user's list of application as application to be predicted.
Step S432, described user is applied in list each application interest-degree respectively with described each application with
The degree of association of described application to be predicted is sued for peace after being multiplied, it is thus achieved that the described user prediction interest-degree to application to be predicted.
I.e. can calculate according to following formula:
Wherein, preRijIt is the described user prediction interest-degree to application j to be predicted, RikIt is that described user is to described user
List of application in the interest-degree of application j, wkjApplication k and application j degree of association, the value of k is from the application of described user
In list, first is applied to last application.
For example, it is assumed that the interest-degree that user 1 is applied in list each application is as shown in the table:
Assume that the degree of association between application two-by-two is as shown in the table:
| Application | Application | Degree of association |
| a | b | 0.41 |
| a | d | 0.71 |
| b | a | 0.41 |
| b | c | 0.67 |
| c | b | 0.67 |
| c | d | 0.58 |
| d | a | 0.71 |
| d | b | 0.29 |
| d | c | 0.58 |
Then can calculate user 1 according to formula as shown in the table to the prediction interest-degree of application to be predicted:
Step S433, using another application in the plurality of user's list of application as application to be predicted, until described
In multiple user's list of applications, the prediction interest-degree of all application all calculates complete.
Step S500, according to described user in all list of applications each application prediction interest-degree and described respectively
Degree of association between individual application and each application special topic, calculates the described user prediction interest-degree to each application special topic.
Wherein, the computational methods of the degree of association between each application described and application special topic, may include that judgement is described and answer
With whether in described application special topic；In described application is included in described application special topic, the most described application is special with described application
The degree of association of topic is 1, and the most described application is 0 with the degree of association of described application special topic.
I.e. can calculate according to equation below:
The embodiment of step S500 has multiple, for example, it is possible to should to each in described application special topic by described user
Prediction interest-degree be multiplied with each application described with the degree of association between described application special topic and sue for peace, it is thus achieved that described user
Prediction interest-degree to described application special topic.
I.e. can calculate according to equation below:
Wherein, userSubjectijRepresent the described user interest-degree to default special topic j.μkjRepresent application k and preset specially
Relation between topic j.
Assume that user 1 is as shown in the table to the prediction interest-degree of application to be predicted:
Each application described and the degree of association preset between special topic are as shown in the table:
Due to special topic ID be 102 special topic in only comprise application a and c, therefore user 1 to special topic ID be 102 special topic
Prediction interest-degree is: 1.67+2.03=3.7.
Due to special topic ID be 105 special topic in only comprise application b, therefore user 1 to special topic ID be 105 special topic prediction
Interest-degree is: 0.75.
Therefore, user 1 is as shown in the table to the prediction interest-degree of described default special topic:
Step S600, according to the described user prediction interest-degree to each application special topic, determines application special topic to be recommended.
Referring to Fig. 8, step S600 may include that
All application special topics are ranked up the prediction interest-degree of application special topic by step S610 according to described user.
Step S620, is defined as application special recommendation to be recommended to described by application special topic the highest for described prediction interest-degree
User.
For example, it is possible to described default special topic is carried out descending sort according to prediction interest-degree, choose forward predetermined number
Default special topic as to be recommended special topic list.
The application special recommendation method that the embodiment of the present invention provides, according to the described user to be recommended application to multiple users
What in list, each was applied predicts interest-degree and each application described and the degree of association preset between special topic, waits to push away described in calculating
Recommend user's prediction interest-degree to described default special topic, and recommend the application being suitable for special according to described prediction interest-degree to user
Topic, in this way, can carry out Auto-matching by default special topic with user, it is possible to recommend different for different users
Special topic, improves Consumer's Experience, and for the platform improving application special recommendation service or application shop, due to not
Operation personnel is needed to go to judge, to which user recommends which special topic, therefore to improve the work efficiency of operation personnel, and then make
The human cost of the platform or application shop that obtain whole application special recommendation service is reduced, and overall operation efficiency obtains
Promote.
Refer to Fig. 9, be the high-level schematic functional block diagram of the application special recommendation device 700 that the embodiment of the present invention provides.Institute
State lower application special recommendation device 700 and include the first computing module 710, the second computing module 720, and determine module 730.
Described first computing module 710, for the list of application according to multiple users, calculates user to all list of applications
In each application prediction interest-degree.
Wherein, the list of application of the plurality of user, install from the plurality of user including by system pre-installation application
Application list in remove after, it is thus achieved that list of application.
Described second computing module 720, for the prediction applied each in all list of applications according to described user
Degree of association between interest-degree and each application described and each application special topic, calculates the described user prediction to each application special topic
Interest-degree.
Wherein, the computational methods of the degree of association between each application described and application special topic, including: judge that described application is
No in described application special topic；In described application is included in described application special topic, the most described application and described application special topic
Degree of association is 1, and the most described application is 0 with the degree of association of described application special topic.
As a kind of embodiment, described second computing module 720, specifically for thematic to described application by described user
In each application prediction interest-degree be multiplied with each application described with the degree of association between described application special topic and sue for peace, obtain
Obtain the described user prediction interest-degree to described application special topic.
Described determine module 730, for according to the described user prediction interest-degree to each application special topic, determine to be recommended should
With special topic.
As a kind of embodiment, described determine computing module 730, specifically for according to described user to application special topic
All application special topics are ranked up by prediction interest-degree；Application special topic the highest for described prediction interest-degree is defined as to be recommended answering
Described user is given with special recommendation.
Refer to Figure 10, Figure 10 and show the first calculating in the application special recommendation device 700 that the embodiment of the present invention provides
The structured flowchart of module 710.Described first computing module 710 can include the first processing module 711, the second processing module 712
And the 3rd processing module 713
Described first processing module 711, in the list of application calculating multiple user, each application is any one with other
Degree of association between individual application.
Described second processing module 712, for calculating the interest-degree of each application that user is applied in list.
As a kind of embodiment, described second processing module 712, specifically for being applied to list according to described user
In the number of operations of each application and default weight corresponding to described operation, calculate described user respectively and be applied in list
The interest-degree of each application.
Described 3rd processing module 713, for any with other according to each application in the plurality of user's list of application
Degree of association and described user between one application are applied in list each interest-degree applied, and calculate described user couple
The prediction interest-degree of each application in all list of applications.
As a kind of embodiment, described 3rd processing module 713, specifically for by the plurality of user's list of application
Any one application as application to be predicted；Described user is applied in list each application interest-degree respectively with institute
State after each application is multiplied with the degree of association of described application to be predicted and sue for peace, it is thus achieved that described user is emerging to the prediction of application to be predicted
Interest degree；Using another application in the plurality of user's list of application as application to be predicted, until the plurality of user applies
The prediction interest-degree of all application in list all calculates complete.
Refer to the first computing module in the application special recommendation device 700 that Figure 11, Figure 11 are embodiment of the present invention offers
The structured flowchart of first processing module 711 of 710.Described first processing module 711 includes the first process submodule 7111, updates
Submodule 7112 and the first calculating sub module 7113.
Described first processes submodule 7111, for according to the application in the list of application of the plurality of user, is formed altogether
Existing matrix, in described co-occurrence matrix, the initial value of matrix element is zero.
Described renewal submodule 7112, for traveling through the list of application of the plurality of user successively, whenever two application are same
Time by a user operation time, the value of the matrix element that said two application is corresponding adds one, until having traveled through the plurality of user
List of application, form new co-occurrence matrix.
Described first calculating sub module 7113, for the value according to the matrix element of described new co-occurrence matrix and each
The numbers of users that matrix element corresponding two application is the most corresponding, calculate in the plurality of user's list of application each application and its
Degree of association between his any one application.
The most each module can be by software code realization, and now, above-mentioned each module can be stored in depositing of server 200
In reservoir 201.The most each module is equally realized by hardware such as IC chip.
It should be noted that each embodiment in this specification all uses the mode gone forward one by one to describe, each embodiment weight
Point explanation is all the difference with other embodiments, and between each embodiment, identical similar part sees mutually.
The application special recommendation device that the embodiment of the present invention is provided, it realizes principle and the technique effect of generation and aforementioned
Embodiment of the method is identical, for briefly describing, and the not mentioned part of device embodiment part, refer in preceding method embodiment corresponding
Content.
In several embodiments provided herein, it should be understood that disclosed apparatus and method, it is also possible to pass through
Other mode realizes.Device embodiment described above is only schematically, such as, and the flow chart in accompanying drawing and block diagram
Show the device of multiple embodiments according to the present invention, the architectural framework in the cards of method and computer program product,
Function and operation.In this, each square frame in flow chart or block diagram can represent a module, program segment or the one of code
Part, a part for described module, program segment or code comprises holding of one or more logic function for realizing regulation
Row instruction.It should also be noted that at some as in the implementation replaced, the function marked in square frame can also be to be different from
The order marked in accompanying drawing occurs.Such as, two continuous print square frames can essentially perform substantially in parallel, and they are the most also
Can perform in the opposite order, this is depending on involved function.It is also noted that every in block diagram and/or flow chart
The combination of the square frame in individual square frame and block diagram and/or flow chart, can be with function or the special base of action performing regulation
System in hardware realizes, or can realize with the combination of specialized hardware with computer instruction.
It addition, each functional module in each embodiment of the present invention can integrate one independent portion of formation
Point, it is also possible to it is modules individualism, it is also possible to two or more modules are integrated to form an independent part.
If described function is using the form realization of software function module and as independent production marketing or use, permissible
It is stored in a computer read/write memory medium.Based on such understanding, technical scheme is the most in other words
The part contributing prior art or the part of this technical scheme can embody with the form of software product, this meter
Calculation machine software product is stored in a storage medium, including some instructions with so that a computer equipment (can be individual
People's computer, server, or the network equipment etc.) perform all or part of step of method described in each embodiment of the present invention.
And aforesaid storage medium includes: USB flash disk, portable hard drive, read only memory (ROM, Read-Only Memory), random access memory
The various media that can store program code such as memorizer (RAM, Random Access Memory), magnetic disc or CD.Need
Being noted that in this article, the relational terms of such as first and second or the like is used merely to an entity or operation
Separate with another entity or operating space, and exist any this between not necessarily requiring or imply these entities or operating
Actual relation or order.And, term " includes ", " comprising " or its any other variant are intended to nonexcludability
Comprise, so that include that the process of a series of key element, method, article or equipment not only include those key elements, but also wrap
Include other key elements being not expressly set out, or also include want intrinsic for this process, method, article or equipment
Element.In the case of there is no more restriction, statement " including ... " key element limited, it is not excluded that including described wanting
Process, method, article or the equipment of element there is also other identical element.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.All within the spirit and principles in the present invention, that is made any repaiies
Change, equivalent, improvement etc., should be included within the scope of the present invention.It should also be noted that similar label and letter exist
Figure below represents similar terms, therefore, the most a certain Xiang Yi accompanying drawing is defined, is then not required in accompanying drawing subsequently
It is defined further and explains.
The above, the only detailed description of the invention of the present invention, but protection scope of the present invention is not limited thereto, and any
Those familiar with the art, in the technical scope that the invention discloses, can readily occur in change or replace, should contain
Cover within protection scope of the present invention.Therefore, protection scope of the present invention should described be as the criterion with scope of the claims.
Claims (19)
1. an application special recommendation method, it is characterised in that described method includes:
According to the list of application of multiple users, calculate user's prediction interest-degree to each application in all list of applications；
And respectively should to prediction interest-degree and each application described of each application in all list of applications according to described user
With the degree of association between special topic, calculate the described user prediction interest-degree to each application special topic；
According to the described user prediction interest-degree to each application special topic, determine application special topic to be recommended.
Method the most according to claim 1, it is characterised in that the described list of application according to multiple users, calculates user
Prediction interest-degree to each application in all list of applications, including:
Calculate the degree of association between each application and other any one application in the list of application of multiple user；
Calculate the interest-degree of each application that user is applied in list；
According to the degree of association between each application in the plurality of user's list of application and other any one application and described
User be applied in list each application interest-degree, calculate described user in all list of applications each application pre-
Survey interest-degree.
Method the most according to claim 2, it is characterised in that described according in the plurality of user's list of application each should
With other, degree of association and described user between any one application are applied in list each interest-degree applied, meter
Calculate described user in all list of applications each application prediction interest-degree, including:
Using any one application in the plurality of user's list of application as application to be predicted；
Described user is applied in list each application interest-degree respectively with described each application with described to be predicted should
Degree of association be multiplied after sue for peace, it is thus achieved that the described user prediction interest-degree to application to be predicted；
Using another application in the plurality of user's list of application as application to be predicted, until the plurality of user applies row
The prediction interest-degree of all application in table all calculates complete.
The most according to the method in claim 2 or 3, it is characterised in that every in described calculating the plurality of user list of application
Degree of association between individual application and other any one application, including:
The application in list of application according to the plurality of user, forms co-occurrence matrix, matrix element in described co-occurrence matrix
Initial value is zero；
Travel through the list of application of the plurality of user successively, when two application are simultaneously by a user operation, said two
The value of the matrix element that application is corresponding adds one, until having traveled through the list of application of the plurality of user, forms new co-occurrence matrix；
The value of the matrix element according to described new co-occurrence matrix and each matrix element corresponding two application correspondence respectively
Number of users, calculate each application in the plurality of user's list of application and the degree of association between other any one application.
The most according to the method in claim 2 or 3, it is characterised in that described calculate user be applied in list each
The interest-degree of application, including:
The number of operations of each application and the default weight that described operation is corresponding it is applied in list according to described user, point
Do not calculate described user and be applied in list the interest-degree of each application.
Method the most according to claim 1, it is characterised in that the degree of association between each application described and application special topic
Computational methods, including:
Judge that described application is whether in described application special topic；
In described application is included in described application special topic, the most described application is 1 with the degree of association of described application special topic, otherwise institute
The degree of association stating application thematic with described application is 0.
7. according to the method described in claim 1 or 6, it is characterised in that described according to described user to described each application
Prediction interest-degree and each degree of association applied and preset between special topic described, calculate described user to described application special topic
Prediction interest-degree, including:
The prediction interest-degree of each application in described application special topic is applied and described application by described user with described each
Degree of association between special topic is multiplied and sues for peace, it is thus achieved that the described user prediction interest-degree to described application special topic.
Method the most according to claim 1, it is characterised in that the list of application of the plurality of user, including pre-by system
Install application from the plurality of user installed the list of application remove after, it is thus achieved that list of application.
Method the most according to claim 1, it is characterised in that the described prediction interest according to described user to application special topic
Degree, determines application special topic to be recommended, including:
According to described user, all application special topics are ranked up by the prediction interest-degree of application special topic；
Application special topic the highest for described prediction interest-degree is defined as application special recommendation to be recommended to described user.
10. an application special recommendation device, it is characterised in that described device includes:
First computing module, for the list of application according to multiple users, calculating user should to each in all list of applications
Prediction interest-degree；
Second computing module, for the prediction interest-degree applied each in all list of applications according to described user and institute
State the degree of association between each application and each application special topic, calculate the described user prediction interest-degree to each application special topic；
Determine module, for applying thematic prediction interest-degree according to described user to each, determine application special topic to be recommended.
11. devices according to claim 10, it is characterised in that described first computing module includes the first processing module,
Second processing module and the 3rd processing module,
Described first processing module, for calculating in the list of application of multiple user each application and other, any one applies it
Between degree of association；
Described second processing module, for calculating the interest-degree of each application that user is applied in list；
Described 3rd processing module, for according to any one application of each application in the plurality of user's list of application and other
Between degree of association and described user be applied in list each application interest-degree, calculate described user to all application
The prediction interest-degree of each application in list.
12. devices according to claim 11, it is characterised in that described 3rd processing module, specifically for by described many
Any one application in individual user's list of application is as application to be predicted；Described user is applied in list each application
Interest-degree respectively with described each application be multiplied with the degree of association of described application to be predicted after summation, it is thus achieved that described user treats
The prediction interest-degree of prediction application；Using another application in the plurality of user's list of application as application to be predicted, until
In the plurality of user's list of application, the prediction interest-degree of all application all calculates complete.
13. according to the device described in claim 11 or 12, it is characterised in that described first processing module includes the first process
Module, updates submodule and the first calculating sub module,
Described first processes submodule, for according to the application in the list of application of the plurality of user, forms co-occurrence matrix, institute
Stating the initial value of matrix element in co-occurrence matrix is zero；
Described renewal submodule, for traveling through the list of application of the plurality of user successively, whenever two application are simultaneously by one
During user operation, the value of the matrix element that said two application is corresponding adds one, until having traveled through the application row of the plurality of user
Table, forms new co-occurrence matrix；
Described first calculating sub module, for value and each matrix element of the matrix element according to described new co-occurrence matrix
The number of users that two corresponding application are the most corresponding, calculates each application in the plurality of user's list of application any one with other
Degree of association between individual application.
14. according to the device described in claim 11 or 12, it is characterised in that described second processing module, specifically for basis
Described user is applied in list the number of operations of each application and the default weight that described operation is corresponding, calculates institute respectively
State user and be applied in list the interest-degree of each application.
15. devices according to claim 10, it is characterised in that the degree of association between each application described and application special topic
Computational methods, including:
Judge that described application is whether in described application special topic；
In described application is included in described application special topic, the most described application is 1 with the degree of association of described application special topic, otherwise institute
The degree of association stating application thematic with described application is 0.
16. according to the device described in claim 10 or 15, it is characterised in that described second computing module, specifically for by institute
State user prediction interest-degree and described each of each application in described application special topic are applied between described application special topic
Degree of association be multiplied and sue for peace, it is thus achieved that described user to described application special topic prediction interest-degree.
17. devices according to claim 10, it is characterised in that the list of application of the plurality of user, including by system
Pre-installation application from the plurality of user installed the list of application remove after, it is thus achieved that list of application.
18. devices according to claim 10, it is characterised in that described determine computing module, specifically for according to described
All application special topics are ranked up by user by the prediction interest-degree of application special topic；By special for application the highest for described prediction interest-degree
Topic is defined as application special recommendation to be recommended to described user.
19. 1 kinds of servers, it is characterised in that described server includes that memorizer and processor, described memorizer are couple to institute
State processor, described memory store instruction, make below described processor execution when executed by the processor
Operation:
According to the list of application of multiple users, calculate user's prediction interest-degree to each application in all list of applications；
And respectively should to prediction interest-degree and each application described of each application in all list of applications according to described user
With the degree of association between special topic, calculate the described user prediction interest-degree to each application special topic；
According to the described user prediction interest-degree to each application special topic, determine application special topic to be recommended.
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| CN201610654844.7A CN106294752A (en) | 2016-08-10 | 2016-08-10 | The application method of special recommendation, device and server |
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