CN108132963A - Resource recommendation method and device, computing device and storage medium - Google Patents
Resource recommendation method and device, computing device and storage medium Download PDFInfo
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Abstract
The present invention discloses a kind of resource recommendation method based on belief network and device, computing device and storage medium.The resource recommendation method includes：The resource adopted using user is trained the data model for being used for resource recommendation, obtains Rating Model of the user to resource to be recommended；Based on the Rating Model, scoring of the user to resource to be recommended is calculated；According to scoring sequence from high to low, resource to be recommended is ranked up；By sort near preceding predetermined quantity resource recommendation to user.Utilize the resource recommendation method and device based on belief network of the present invention, it can be ensured that the resource set of recommendation is maximized by the success rate that user adopts, so as to improve the personalization level of recommendation results.
Description
Technical field
The present invention relates to resource recommendation technical field, more particularly to a kind of resource recommendation method based on belief network and
Device, computing device and storage medium.
Background technology
With the development of Internet technology, the information content on internet is growing day by day.In face of the information of magnanimity, how is user
It rapidly searches for becoming very difficult to oneself required information.Resource recommendation technology is a kind of important information filtering means,
Can the interested information of user automatically be searched by information filtering, so as to provide effective personalized clothes to the user
Business.For example, during the operation of application shop, there are some scenes to need to like recommendation a batch application according to user interest, such as：
" guessing that you like ".
Existing resource recommendation technology can be divided into three kinds of content-based recommendation, collaborative filtering, mixing recommendation recommendation skills
Art.Content-based recommendation technology refers to the preference information according to user's history, recommends the resource with like attribute.It is insufficient
In terms of part is the unicity of recommendation resource and there are problems that the Content Feature Extraction to multimedia resource, therefore the technology
It is chiefly used in the recommendation of web page resources.Collaborative filtering recommending technology has the user group of same interest hobby by searching with user,
The resource that other users are liked into user's recommended user's group.For example, collaborative filtering recommending technology is used to be given according to current application
User recommends the relevant application of a batch, specially：Limit applying for recommendation first is having same label, Ran Houtong with intended application
It crosses the download of user, browsing, the user behavior space vector that behavioral datas establish each application has been installed etc., finally according to cosine system
Number (or the German number of outstanding card, Pearson's coefficient etc.) calculates the similarity value for recommending application and intended application, takes similarity ranking most
A batch application of front is as recommendation collection.Collaborative filtering recommending technology is there is also many problems, for example, to new user or new resources
Cold start-up problem during recommendation, the sparse sex chromosome mosaicism of score data and the scalability problem of algorithm etc..Mix recommended technology
It is that both the above recommended technology is applied in combination, it is intended to make up the deficiency of various recommended technologies.So far, collaborative filtering recommending skill
Art is one of recommended technology the most successful in e-commerce field.But in the individualized resource field for being divided into target with higher assessment
Scape, traditional collaborative filtering recommending technology are to define similarity between article by cosine relative coefficient, and this mode is simultaneously
It cannot be guaranteed that the optimal solution of high scoring.
Invention content
In order to overcome the above-mentioned deficiency in collaborative filtering recommending technology, the present invention provides a kind of resource based on belief network
Recommend method and apparatus, to ensure that the resource set recommended is maximized by the success rate that user adopts.
To achieve these goals, the first aspect of the present invention provides a kind of resource recommendation method based on belief network,
The resource recommendation method includes the following steps：The resource adopted using user carries out the data model for being used for resource recommendation
Training, obtains Rating Model of the user to resource to be recommended；Based on the Rating Model, calculate user and resource to be recommended is commented
Point；According to scoring sequence from high to low, resource to be recommended is ranked up；The resource to sort near preceding predetermined quantity is pushed away
It recommends to user.
The second aspect of the present invention provides a kind of resource recommendation device based on belief network, the resource recommendation device packet
It includes：Training module for being trained using the resource that user has adopted to the data model for being used for resource recommendation, obtains user
To the Rating Model of resource to be recommended；Computing module comments resource to be recommended for calculating user based on the Rating Model
Point；Sorting module, for being ranked up according to scoring sequence from high to low to resource to be recommended；Recommending module, for that will arrange
Sequence near preceding predetermined quantity resource recommendation to user.
The third aspect of the present invention provides a kind of computing device, which includes：Processor；And memory,
On be stored with executable code, when the executable code is performed by the processor, the processor is made to perform the present invention
The resource recommendation method that is provided of first aspect.
The fourth aspect of the present invention provides a kind of non-transitory machinable medium, is stored thereon with executable generation
Code when the executable code is performed by the processor of electronic equipment, makes the processor perform the first aspect of the present invention
The resource recommendation method provided.
Technical scheme of the present invention, the resource adopted by using user, to be used for the data model of resource recommendation into
Row training, obtains Rating Model of the user to resource to be recommended, it is ensured that the success rate that the resource set of recommendation is adopted by user is maximum
Change, so as to improve the personalization level of recommendation results.
Description of the drawings
Exemplary embodiment of the invention is described in more detail in conjunction with the accompanying drawings, it is of the invention above-mentioned and its
Its purpose, feature and advantage will be apparent, wherein, in exemplary embodiment of the invention, identical reference label
Typically represent same parts.
Fig. 1 is the flow chart of the resource recommendation method according to the present invention based on belief network.
Fig. 2 is the schematic diagram of the belief network of resource built according to the present invention.
Fig. 3 is the structure diagram of the resource recommendation device according to the present invention based on belief network.
Specific embodiment
The preferred embodiment of the present invention is more fully described below with reference to accompanying drawings.Although the present invention is shown in attached drawing
Preferred embodiment, however, it is to be appreciated that may be realized in various forms the present invention without the embodiment party that should be illustrated here
Formula is limited.On the contrary, these embodiments are provided so that the present invention is more thorough and complete, and can be by the present invention's
Range is completely communicated to those skilled in the art.
Before technical scheme of the present invention is specifically described, the term mentioned in the present invention is fitted first
When explanation." resource " mentioned herein refers to so that computer or processor or other function execution means are able to carry out
The component software or hardware mechanism of specific function, including multimedia (for example, audio, video, picture etc.), document, using and
Other online Internet resources." belief network " mentioned herein refers to a kind of uncertainty based on probability analysis, graph theory
The model of expression and the reasoning of knowledge, also referred to as Bayesian network, Belief Network or causal net.It is mentioned herein that " electronics is set
It is standby " refer to it is any with the equipment of electronic unit used by terminal user or consumer, including mobile phone, personal digital assistant
(PDA), computer, tablet computer, music player, camera, video recorder, electronic reader, radio set equipment and trip
Gaming machine.
In order to make technical scheme of the present invention clearer, clear, below with reference to accompanying drawings and combine to electronic equipment use
Recommend the example of resource that the preferred embodiment of the present invention is described in detail in family.
Fig. 1 is the flow chart of the resource recommendation method according to the present invention based on belief network.The resource recommendation method can
Suitable for recommending the situation of online Internet resources to electronic device user, include but not limited to recommend multimedia, document, application
The situation of APP.As shown in Figure 1, the resource recommendation method according to the present invention based on belief network includes：
Step S101：The resource adopted using user is trained the data model for being used for resource recommendation, is used
Family is to the Rating Model of resource to be recommended.The step can also be referred to as the model training stage.For example, (compare to electronic equipment
Such as, mobile phone) user recommend application in the case of, the stage can by using installed application build application belief network.Tool
Body, based on the assumption that build the belief network of resource shown in Fig. 2：User adopts the probability of a certain resource recommendation by working as
The preceding all real estate impacts adopted, that is, if user has adopted the recommendation of resource, then user will adopt resource
Probability is influenced by the resource adopted.For example, in the case where recommending application to electronic equipment (for example, mobile phone) user,
The probability of a certain resource of user installation influenced by all applications being currently installed on, that is, if user installation, using A, B, C are so
The probability of user installation application D is by installation using A, the influence of B, C.
Assuming that U represents all resource sample sets, u represents user, and m represents the number of all resources in resources bank, i, j tables
Show resource to be recommended, qj,iRepresent belief network parameter, lu,jRepresent normalized scores of the user u to resource j, that is,Here, vu,jIt represents whether user u has adopted resource j, works as vu,jRepresent that user u has adopted resource when=0
J works as vu,jRepresent that user u does not adopt resource j when=1.scoreu,iRepresent user u to the scoring of resource i (for example, for table
Requisition family u likes degree to resource i), calculation formula is as follows：
There is certain stable, pervasive relationship, i.e., the relationship q between resource and resource between resource and resourcej,iFor not
It is consistent with user.Build belief network process it is practical be exactly correlation q between computing resource and resourcej,i.Separately
Outside, it is noted that, due to considering user behavior path, so the belief network of resource is digraph, i.e. qj,i≠qi,j.For example, with
Family is mounted with the probability using B using installation after A, is using the probability of A not equal to installation after user installation application B.Then,
The parameter q of belief network is calculated by following two stepj,i：
The first step：Likelihood function (also referred to as loss function) g (q) is constructed, i.e.,
Second step：Seek parameter q during minimum value (object function is min g (q)) of likelihood functionj,iTo get arriving resource
Belief network.
In the present embodiment the parameter q during minimum value of likelihood function is sought using gradient descent methodj,i, still, this field
Technical staff can also seek the parameter q during minimum value of likelihood function using other equivalent algorithms known in the fieldj,i。
Gradient descent method is specially as follows：
Step (a)：One group of transfer matrix { q between 0 to 1 is given at randomi,j| i, j ∈ I }, it is set as q(0), initialization changes
Ride instead of walk several k=0；
Step (b)：Iterative calculationWherein θ is the step number of iteration, for example, can be with
Take θ=0.01；
Step (c)：Judge Δ g (q(k+1))=| g (q(k+1))-g(q(k)) | whether restrain, if | Δ g (q(k+1))-Δg(q(k)) | ＜ α then return to q(k+1), otherwise the transfer matrix as estimated back to step (b) and continues to calculate, wherein α is one
The value of very little, for example, the θ of α=0.01 can be taken.
Step S102：Based on the obtained Rating Models of step S101, i.e.,Calculate user
Scoring to resource to be recommended.For example, in the case where recommending application to electronic equipment (for example, mobile phone) user, the scoring mould
Type is clicking rate model of the user to different application.According to above-mentioned formula, based on different application and the corresponding feature of user to
Amount, you can obtain click probability of the user to different application.
Step S103：According to scoring sequence from high to low, resource to be recommended is ranked up.
Step S104：By sort near preceding predetermined quantity resource recommendation to user.
For example, in the case where recommending application to electronic equipment (for example, mobile phone) user, if all application collection of resources bank
A is combined into, can estimate user in advance by step S101 and step S102 clicks clicking rate of any one in set A using b
scoreu,i.Then, according to clicking rate scoreu,iDescending is done to set A, intercept near 100 preceding applications and is recommended
User.
Above resource to be recommended to be pushed away as example to describe the resource based on belief network of the present invention to electronic device user
Recommend method.Those skilled in the art can along the exemplary thinking under other scenes using the present invention based on conviction net
The resource recommendation method of network.
Compared to traditional collaborative filtering method, the resource recommendation method based on belief network of the present embodiment passes through construction
Loss function simultaneously asks the minimum value of loss function to acquire optimal solution, it is ensured that the success rate that the resource set of recommendation is adopted by user is most
Bigization, so as to improve the personalization level of recommendation results.Specifically, in the case where recommending application to electronic device user, lead to
It crosses construction installation rate and estimates function, then construct loss function, optimal solution is acquired by the minimum value for seeking loss function, it is ensured that this
The recommendation collection that kind mode calculates is mounted to power maximization.Moreover, the present embodiment by be exposed to the conversion ratio of installation compared to
Traditional collaborative filtering method has to be promoted by a relatively large margin.
In addition, the present invention also provides a kind of resource recommendations for being used to implement the above-mentioned resource recommendation method based on belief network
Device.As shown in figure 3, resource recommendation device 300 includes training module 301, computing module 302, sorting module 303 and recommends mould
Block 304.
Training module 301 is used to be trained the data model for being used for resource recommendation using the resource that user has adopted,
Obtain Rating Model of the user to resource to be recommended.Rating Model can be expressed as：
Wherein, u represents user, and m represents the number of all resources in resources bank, and i, j represent resource to be recommended, qj,iIt represents
Belief network parameter, lu,jRepresent normalized scores of the user u to resource j, that is,Here, vu,jIt represents to use
Whether family u has adopted resource j, works as vu,jRepresent that user u has adopted resource j, works as v when=0u,jRepresent that user u does not adopt when=1
Resource j.
Computing module 302 is used to calculate scoring of the user to resource to be recommended based on the Rating Model.Computing module can be wrapped specifically
It includes：Structural unit, for constructing likelihood function g (q), i.e.,
Wherein, U represents all resource sample sets；Unit is solved, for seeking the parameter q during minimum value of likelihood functionj,i.It solves single
Member can seek the parameter q during minimum value of likelihood function g (q) by performing following stepsj,i：
Step (a)：One group of transfer matrix { q between 0 to 1 is given at randomi,j| i, j ∈ I }, it is set as q(0), initialization changes
Ride instead of walk several k=0；
Step (b)：Iterative calculationWherein θ is the step number of iteration, for example, can be with
Take θ=0.01；
Step (c)：Judge Δ g (q(k+1))=| g (q(k+1))-g(q(k)) | whether restrain, if | Δ g (q(k+1))-Δg(q(k)) | ＜ α then return to q(k+1), otherwise the transfer matrix as estimated back to step (b) and continues to calculate, wherein α is one
The value of very little, for example, the θ of α=0.01 can be taken.
Sorting module 303 is used to be ranked up resource to be recommended according to scoring sequence from high to low.Recommending module 304
For the resource recommendation that will sort near preceding predetermined quantity to user.
Above by reference to attached drawing be described in detail the resource recommendation method according to the present invention based on belief network and
Device.
In addition, it is also implemented as a kind of computer program or computer program product according to the method for the present invention, the meter
Calculation machine program or computer program product include the calculating of above steps limited in the above method for performing the present invention
Machine program code instruction.
In addition, the present invention can also be embodied as a kind of computing device, which includes：Processor；And memory,
Executable code is stored thereon with, when the executable code is performed by the processor, the processor is made to perform basis
Each step of the method for the present invention.
Alternatively, the present invention can also be embodied as a kind of (or the computer-readable storage of non-transitory machinable medium
Medium or machine readable storage medium), executable code (or computer program or computer instruction code) is stored thereon with,
When the executable code (or computer program or computer instruction code) is by electronic equipment (or computing device, server
When) processor perform when, the processor is made to perform each step of the above method according to the present invention.
Those skilled in the art will also understand is that, with reference to the described various illustrative logical blocks of disclosure herein, mould
Block, circuit and algorithm steps may be implemented as the combination of electronic hardware, computer software or both.
Flow chart and block diagram in attached drawing show that the possibility of the system and method for multiple embodiments according to the present invention is real
Existing architectural framework, function and operation.In this regard, each box in flow chart or block diagram can represent module, a journey
A part for sequence section or code, as defined in the part of the module, program segment or code is used to implement comprising one or more
The executable instruction of logic function.It should also be noted that in some implementations as replacements, the function of being marked in box also may be used
To be occurred with being different from the sequence marked in attached drawing.For example, two continuous boxes can essentially perform substantially in parallel,
They can also be performed in the opposite order sometimes, this is depended on the functions involved.It is also noted that block diagram and/or stream
The combination of each box in journey figure and the box in block diagram and/or flow chart can use functions or operations as defined in performing
Dedicated hardware based system realize or can be realized with the combination of specialized hardware and computer instruction.
Various embodiments of the present invention are described above, above description is exemplary, and non-exclusive, and
It is not limited to disclosed each embodiment.In the case of without departing from the scope and spirit of illustrated each embodiment, for this skill
Many modifications and changes will be apparent from for the those of ordinary skill in art field.The selection of term used herein, purport
In the principle for best explaining each embodiment, practical application or to the improvement of the technology in market or make the art
Other those of ordinary skill are understood that each embodiment disclosed herein.
Claims (14)
1. a kind of resource recommendation method based on belief network, includes the following steps：
The resource adopted using user is trained the data model for being used for resource recommendation, obtains user to money to be recommended
The Rating Model in source；
Based on the Rating Model, scoring of the user to resource to be recommended is calculated；
According to scoring sequence from high to low, resource to be recommended is ranked up；
By sort near preceding predetermined quantity resource recommendation to user.
2. resource recommendation method according to claim 1, which is characterized in that the Rating Model is expressed as：
Wherein, u represents user, and m represents the number of all resources in resources bank, and i, j represent resource to be recommended, qj,iRepresent conviction
Network parameter, lu,jRepresent normalized scores of the user u to resource j, that is,Here, vu,jRepresenting user u is
It is no to have adopted resource j, work as vu,jRepresent that user u has adopted resource j, works as v when=0u,jRepresent that user u does not adopt resource when=1
j。
3. resource recommendation method according to claim 2, which is characterized in that acquire belief network parameter as follows
qj,i：
Likelihood function g (q) is constructed, i.e.,Wherein, U
Represent all resource sample sets；
Seek the parameter q during minimum value of likelihood functionj,i。
4. resource recommendation method according to claim 3, which is characterized in that seek likelihood function g (q) as follows
Minimum value when parameter qj,i：
Step (a)：One group of transfer matrix { q between 0 to 1 is given at randomi,j| i, j ∈ I }, it is set as q(0), initialize iteration step
Number k=0；
Step (b)：Iterative calculationWherein θ is the step number of iteration；
Step (c)：Judge Δ g (q(k+1))=| g (q(k+1))-g(q(k)) | whether restrain, if | Δ g (q(k+1))-Δg(q(k))|
＜ α, then return to q(k+1), otherwise the transfer matrix as estimated back to step (b) and continues to calculate, wherein α is a very little
Value.
5. resource recommendation method according to any one of claim 1 to 4, which is characterized in that the resource includes more matchmakers
Body, document, using and other online Internet resources, also, it is described scoring correspond to user to the click of the resource, download,
The probability of browsing, installation or collection.
6. resource recommendation method according to any one of claim 1 to 4, which is characterized in that the resource is to electronics
The application of equipment recommendation, also, the scoring corresponds to click probability of the user to the application.
7. a kind of resource recommendation device based on belief network, including：
Training module for being trained using the resource that user has adopted to the data model for being used for resource recommendation, is used
Family is to the Rating Model of resource to be recommended；
Computing module, for calculating scoring of the user to resource to be recommended based on the Rating Model；
Sorting module, for being ranked up according to scoring sequence from high to low to resource to be recommended；
Recommending module, for the resource recommendation that will sort near preceding predetermined quantity to user.
8. resource recommendation device according to claim 7, which is characterized in that the Rating Model is expressed as：
Wherein, u represents user, and m represents the number of all resources in resources bank, and i, j represent resource to be recommended, qj,iRepresent conviction
Network parameter, lu,jRepresent normalized scores of the user u to resource j, that is,Here, vu,jRepresenting user u is
It is no to have adopted resource j, work as vu,jRepresent that user u has adopted resource j, works as v when=0u,jRepresent that user u does not adopt resource when=1
j。
9. resource recommendation device according to claim 8, which is characterized in that the computing module includes：
Structural unit, for constructing likelihood function g (q), i.e.,
Wherein, U represents all resource sample sets；
Unit is solved, for seeking the parameter q during minimum value of likelihood functionj,i。
10. resource recommendation device according to claim 9, which is characterized in that the solution unit is by performing following step
It is rapid to seek the parameter q during minimum value of likelihood function g (q)j,i：
Step (a)：One group of transfer matrix { q between 0 to 1 is given at randomi,j| i, j ∈ I }, it is set as q(0), initialize iteration step
Number k=0；
Step (b)：Iterative calculationWherein θ is the step number of iteration；
Step (c)：Judge Δ g (q(k+1))=| g (q(k+1))-g(q(k)) | whether restrain, if | Δ g (q(k+1))-Δg(q(k))|
＜ α, then return to q(k+1), otherwise the transfer matrix as estimated back to step (b) and continues to calculate, wherein α is a very little
Value.
11. the resource recommendation device according to any one of claim 7 to 10, which is characterized in that the resource includes more
Media, document, using and other online Internet resources, also, the scoring correspond to user to the click of the resource, under
It carries, browsing, the probability installed or collected.
12. the resource recommendation device according to any one of claim 7 to 10, which is characterized in that the resource is to electricity
The application of sub- equipment recommendation, also, the scoring corresponds to click probability of the user to the application.
13. a kind of computing device, including：
Processor；And
Memory is stored thereon with executable code, when the executable code is performed by the processor, makes the processing
Device performs the method as any one of claim 1-6.
14. a kind of non-transitory machinable medium, is stored thereon with executable code, when the executable code is electric
When the processor of sub- equipment performs, the processor is made to perform such as method according to any one of claims 1 to 6.
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| CN108804670A (en) * | 2018-06-11 | 2018-11-13 | 腾讯科技（深圳）有限公司 | Data recommendation method, device, computer equipment and storage medium |
| CN110443636A (en) * | 2019-07-15 | 2019-11-12 | 阿里巴巴集团控股有限公司 | Resource is distributed between user to regulate and control the method and apparatus of its e-payment behavior |
| CN110889029A (en) * | 2018-08-17 | 2020-03-17 | 北京京东金融科技控股有限公司 | City target recommendation method and device |
| CN110941727A (en) * | 2019-11-29 | 2020-03-31 | 北京达佳互联信息技术有限公司 | Resource recommendation method and device, electronic equipment and storage medium |
| CN110995828A (en) * | 2019-11-29 | 2020-04-10 | 北京邮电大学 | Network resource caching method, device and system |
| CN111191142A (en) * | 2018-11-14 | 2020-05-22 | 腾讯科技（深圳）有限公司 | Electronic resource recommendation method and device and readable medium |
| CN111340522A (en) * | 2019-12-30 | 2020-06-26 | 支付宝实验室(新加坡)有限公司 | Resource recommendation method, device, server and storage medium |
| CN111625713A (en) * | 2020-04-30 | 2020-09-04 | 平安国际智慧城市科技股份有限公司 | Resource recommendation method and device based on big data, electronic equipment and medium |
| CN112199187A (en) * | 2019-07-08 | 2021-01-08 | 北京字节跳动网络技术有限公司 | Content display method and device, electronic equipment and computer readable storage medium |
| CN116701772A (en) * | 2023-08-03 | 2023-09-05 | 广东美的暖通设备有限公司 | Data recommendation method and device, computer readable storage medium and electronic equipment |
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