CN109670892A - A kind of collaborative filtering recommending method and system, terminal device - Google Patents
A kind of collaborative filtering recommending method and system, terminal device Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
Abstract
The present invention is suitable for data mining technology field, provide a kind of collaborative filtering recommending method and system, terminal device, including being pre-processed at least one data source, filter out effective label of at least one data source, the behavioural characteristic that user is obtained according to effective label of all data sources, the hobby label in all auxiliary data sources is analyzed according to the behavioural characteristic of user；The hobby label of the hobby label in all auxiliary data sources and target data source is established into incidence relation；The hobby label of target data source is obtained by the hobby label in auxiliary data source；Recommendation results are generated according to the hobby label of target data source, it is predicted by hobby of multiple data sources to user, effectively improve the accuracy rate of prediction, it avoids due to Sparse and leads to deviation occur in prediction user preferences, the data of multiple data sources recommend the user being newly added and the Xin commodity being added, and efficiently solving existing Collaborative Filtering Recommendation System has recommendation results inaccuracy.
Description
Technical field
The invention belongs to data mining technology fields more particularly to a kind of collaborative filtering recommending method and system, terminal to set
It is standby.
Background technique
Collaborative Filtering Recommendation System can excavate user according to the historical behavior of user record from mass data may
Favorite content, and by the commending contents of user preference to user.By being clustered to user and commodity, make each user and
Every commodity belong to some class, then carry out collaborative filtering to class cluster, introduce singularity value decomposition, are merged using graph model
Recommended, effectively to the possible favorite content of user recommended user, however existing Collaborative Filtering Recommendation System is due to number
Occur deviation when predicting user preferences according to sparse will lead to, can also be gone through since the user being newly added and the Xin commodity being added lack
History behavior record, and effectively the user being newly added and the Xin commodity being added can not be recommended.
In conclusion there is recommendation results inaccuracy in existing Collaborative Filtering Recommendation System.
Summary of the invention
In view of this, the embodiment of the invention provides a kind of collaborative filtering recommending method and system, terminal device, to solve
The problems of the prior art.
The first aspect of the embodiment of the present invention provides a kind of collaborative filtering recommending method, comprising:
At least one data source is pre-processed, filters out effective label of at least one data source, it is described extremely
A few data source includes auxiliary data source；
The behavioural characteristic that user is obtained according to effective label of all data sources is analyzed according to the behavioural characteristic of the user
The hobby label in all auxiliary data sources；
The hobby label of the hobby label in all auxiliary data sources and target data source is established into incidence relation；
According to the incidence relation, the hobby label of target data source is obtained by the hobby label in auxiliary data source；
Recommendation results are generated according to the hobby label of the target data source.
The second aspect of the embodiment of the present invention provides a kind of Collaborative Filtering Recommendation System, comprising:
Screening module filters out having at least one data source for pre-processing at least one data source
Criterion label, at least one described data source include auxiliary data source；
Analysis module, for obtaining the behavioural characteristic of user according to effective label of all data sources, according to the user
Behavioural characteristic analyze the hobby label in all auxiliary data sources；
Relationship establishes module, for by all auxiliary data sources hobby label and target data source hobby label
Establish incidence relation；
Module is obtained, for obtaining target data source by the hobby label in auxiliary data source according to the incidence relation
Hobby label；
Result-generation module, for generating recommendation results according to the hobby label of the target data source.
The third aspect of the embodiment of the present invention provides a kind of terminal device, including memory, processor and is stored in
In the memory and the computer program that can run on the processor, when the processor executes the computer program
It performs the steps of
At least one data source is pre-processed, filters out effective label of at least one data source, it is described extremely
A few data source includes auxiliary data source；
The behavioural characteristic that user is obtained according to effective label of all data sources is analyzed according to the behavioural characteristic of the user
The hobby label in all auxiliary data sources；
The hobby label of the hobby label in all auxiliary data sources and target data source is established into incidence relation；
According to the incidence relation, the hobby label of target data source is obtained by the hobby label in auxiliary data source；
Recommendation results are generated according to the hobby label of the target data source.
The fourth aspect of the embodiment of the present invention provides a kind of computer readable storage medium, the computer-readable storage
Media storage has computer program, and the computer program performs the steps of when being executed by processor
At least one data source is pre-processed, filters out effective label of at least one data source, it is described extremely
A few data source includes auxiliary data source；
The behavioural characteristic that user is obtained according to effective label of all data sources is analyzed according to the behavioural characteristic of the user
The hobby label in all auxiliary data sources；
The hobby label of the hobby label in all auxiliary data sources and target data source is established into incidence relation；
According to the incidence relation, the hobby label of target data source is obtained by the hobby label in auxiliary data source；
Recommendation results are generated according to the hobby label of the target data source.
Collaborative filtering recommending method provided by the invention and system, terminal device are by establishing the hobby mark of target data source
The incidence relation of label and the hobby label in all auxiliary data sources, is calculated by the hobby label in all auxiliary data sources and obtains mesh
The hobby label for marking data source generates recommendation results, recommends user, is carried out by hobby of multiple data sources to user
Prediction, can effectively improve the accuracy rate of prediction, avoid due to Sparse and cause to occur in prediction user preferences inclined
Difference, and the user being newly added and the Xin commodity being added can be recommended by the data of multiple data sources, effectively solve
There is recommendation results inaccuracy in certainly existing Collaborative Filtering Recommendation System.
Detailed description of the invention
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art
Needed in attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is only of the invention some
Embodiment for those of ordinary skill in the art without any creative labor, can also be according to these
Attached drawing obtains other attached drawings.
Fig. 1 is a kind of implementation process schematic diagram for collaborative filtering recommending method that the embodiment of the present invention one provides；
Fig. 2 is the implementation process schematic diagram of one step S101 of corresponding embodiment provided by Embodiment 2 of the present invention；
Fig. 3 is the implementation process schematic diagram for the one step S104 of corresponding embodiment that the embodiment of the present invention three provides；
Fig. 4 is the implementation process schematic diagram for the one step S105 of corresponding embodiment that the embodiment of the present invention four provides；
Fig. 5 is a kind of structural schematic diagram for Collaborative Filtering Recommendation System that the embodiment of the present invention five provides；
Fig. 6 is the structural schematic diagram of screening module 101 in the corresponding embodiment five of the offer of the embodiment of the present invention six；
Fig. 7 is the structural schematic diagram that module 104 is obtained in the corresponding embodiment five of the offer of the embodiment of the present invention seven；
Fig. 8 is 105 structural schematic diagram of result-generation module in the corresponding embodiment five that the embodiment of the present invention eight provides；
Fig. 9 is the schematic diagram for the terminal device that the embodiment of the present invention nine provides.
Specific embodiment
In being described below, for illustration and not for limitation, the tool of such as particular system structure, technology etc is proposed
Body details, to understand thoroughly the embodiment of the present invention.However, it will be clear to one skilled in the art that there is no these specific
The present invention also may be implemented in the other embodiments of details.In other situations, it omits to well-known system, device, electricity
The detailed description of road and method, in case unnecessary details interferes description of the invention.
Collaborative filtering recommending method provided in an embodiment of the present invention and system, terminal device, by establishing target data source
Hobby label and all auxiliary data sources hobby label incidence relation, pass through the hobby label meter in all auxiliary data sources
The hobby label generation recommendation results for obtaining target data source are calculated, user are recommended, by multiple data sources to user's
Hobby is predicted, the accuracy rate of prediction can be effectively improved, and is avoided due to Sparse and is caused in prediction user's happiness
Occur deviation well, and the user being newly added and the Xin commodity being added can be recommended by the data of multiple data sources,
Efficiently solving existing Collaborative Filtering Recommendation System has recommendation results inaccuracy.
In order to illustrate technical solutions according to the invention, the following is a description of specific embodiments.
Embodiment one:
As shown in Figure 1, being specifically included the present embodiment provides a kind of collaborative filtering recommending method step:
Step S101: pre-processing at least one data source, and filter out at least one data source has criterion
Label, at least one described data source includes auxiliary data source.
In a particular application, the effective label for screening at least one data source is by the society at least one data source
Change label to be screened.
In a particular application, socialized label refers to interconnection user on the network according to itself impression to product, by product
Certain attribute is carried out customized with a certain label, and socialized label can represent the attribute of article to a certain extent.Due to society
It is to be customized by the user generation, therefore there is the mark that part is not used to prediction user preferences in socialized label that label, which can be changed,
Label need for this part in data source to be not used to predict that the socialized label of user preferences screens out, and then filter out
Effective label of data source.
In the present embodiment, at least one above-mentioned data source includes auxiliary data source, further, at least one above-mentioned number
It can also include target data source according to source.
Illustratively, above-mentioned target data source is cinematic data, and above-mentioned auxiliary data source is music data.Pre-process film
The socialized label of the socialized label and music data (auxiliary data source) of data (target data source), then filters out film
Effective label of data and effective label of music data.
Step S102: the behavioural characteristic of user is obtained according to effective label of all data sources, according to the row of the user
It is characterized the hobby label for analyzing all auxiliary data sources.
In one embodiment, step S102 includes:
Pass through formula (1)The effective label for obtaining all data sources is related to product
Property；
Pass through formula (2)User is calculated to effective label in individual data source
Favorable rating；
The hobby label in all auxiliary data sources is obtained according to favorable rating analysis；
Wherein, TFi,tIndicate the frequency that effective label t is labeled in product i, N represents article all in the data source
Total amount, n represent include in the data source label t number of articles, ratingu,iUser u is represented to product in the data source
The history of i scores, and M is the intersection that all commodity in the single data source of effective label t are marked with by user.
Illustratively, user is higher to the mark frequency of effective label t of the product i in the data source, then it represents that this
The favorable rating of effective label t is higher, therefore using effective label t as liking label.
Step S103: the hobby label in all auxiliary data sources is associated with the foundation of the hobby label of target data source
Relationship.
In a particular application, the hobby in all auxiliary data sources is excavated by FP-Growth association rule algorithm
Correlation rule between label and the hobby label of the target data source, establishes incidence relation according to the correlation rule.?
In similar field, user possesses similar hobby, so according to the hobby label of the data source of the different field of multiple users
(the hobby labels of all data sources) excavate the correlation rule of the hobby label of different field, further establish described all
The hobby label in auxiliary data source and the hobby label of target data source establish incidence relation.
Step S104: according to the incidence relation, the happiness of target data source is obtained by the hobby label in auxiliary data source
Good label.
Step S105: recommendation results are generated according to the hobby label of the target data source.
In a particular application, hobby classification of the hobby tag representation user of target data source in target data source, because
This can generate corresponding recommendation results according to the hobby label of the target data source.
The collaborative filtering recommending method of the present embodiment by establish target data source hobby label and all auxiliary datas
The incidence relation of the hobby label in source calculates the hobby mark for obtaining target data source by the hobby label in all auxiliary data sources
Label generate recommendation results, recommend user, are predicted, can effectively be mentioned by hobby of multiple data sources to user
The accuracy rate of height prediction, avoids due to Sparse and leads to deviation occur in prediction user preferences, and can be by more
The data of a data source recommend the user being newly added and the Xin commodity being added, and efficiently solve existing collaborative filtering and push away
There is recommendation results inaccuracy in the system of recommending.
Embodiment two
As shown in Fig. 2, in one embodiment of the invention, the step S101 in embodiment corresponding to Fig. 1 is specifically wrapped
It includes:
Step S201: the socialized label of at least one data source is obtained.
In a particular application, the socialized label for obtaining at least one data source includes obtaining at least one auxiliary data source
Socialized label.
Step S202: the socialized label of at least one data source is screened to obtain by default screening conditions
Take effective label.
In a particular application, the socialized label for meeting the data source of default screening conditions is screened out, and then sieved
Select effective label of the data source.
In a particular application, above-mentioned default screening conditions are the label for representing personal preference or ownness, such as " are pushed away strongly
Recommend ", " good-looking ", the labels such as " good book, be worth recommend ".
In a particular application, above-mentioned default screening conditions are that the socialized label frequency of occurrence is less than default screening threshold value
Label, wherein default screening threshold value be in order to screen out under-represented label and pre-set label label number, if
The frequency of occurrence of socialized label is less than default screening threshold value, then illustrates that the label does not have representativeness.
In a particular application, above-mentioned default screening conditions are meaningless label, such as the viewing date label of user.
In a particular application, above-mentioned default screening conditions are, by the frequency of occurrence of all socialized labels of the data source
It is arranged according to from more to few sequence, chooses top n socialized label as effective label, in a particular application, N can be with
It is configured as needed.
Embodiment three:
As shown in figure 3, in one embodiment of the invention, the step S103 in embodiment corresponding to Fig. 1 is specifically wrapped
It includes:
Step S301 estimates hobby according to the hobby label in auxiliary data source calculating target data source based on incidence relation
Label.
It in the present embodiment, can be according to the hobby label at least one auxiliary data source by the incidence relation of foundation
Calculate target data source estimates hobby label.Hobby label is estimated to refer to and got according to the hobby label in auxiliary data source
Target data source in all possibility like label.
The target data source for meeting predetermined selection condition is estimated hobby label as the target data by step S202
The hobby label in source.
In a particular application, above-mentioned predetermined selection condition is, by the above-mentioned frequency of occurrence for estimating hobby label according to from more
It is arranged to few sequence, hobby label of the hobby label as the target data source is estimated for M before choosing, in concrete application
In, M, which can according to need, to be configured.
Example IV:
As shown in figure 4, in one embodiment of the invention, the step S105 in embodiment corresponding to Fig. 1 is specifically wrapped
It includes:
Step S401 is filtered target data source by collaborative filtering.
The product for not including target data source hobby label is filtered to obtain recommendation results by step S402.
In the present embodiment, since the hobby label of the target data source obtained according to incidence relation is often more public,
Many products all include these hobby labels, i.e. the hobby label of target data source only indicate user target data source substantially
Like classification, therefore, it is necessary to combine collaborative filtering to generate recommendation results.
Step S403 recommends the recommendation results for meeting default recommendation condition to user.
In the present embodiment, after generating recommendation results, after further recommendation results are screened, then to user into
Row is recommended, in a particular application, after generating recommendation results, according to the frequency of occurrence for liking label in recommendation results, according to
It is arranged from more to few sequence, X recommendation results are recommended to user before choosing.
Embodiment five
As shown in fig. 6, one embodiment of the present of invention provides a kind of video searching system 100, for executing corresponding to Fig. 1
Embodiment in method and step comprising:
Screening module 101 filters out at least one data source for pre-processing at least one data source
Effective label, at least one described data source include auxiliary data source.
Analysis module 102 is used to obtain the behavioural characteristic of user according to effective label of all data sources, according to the use
The behavioural characteristic at family analyzes the hobby label in all auxiliary data sources.
Relationship is established module 103 and is used for the hobby mark of the hobby label and target data source in all auxiliary data sources
Label establish incidence relation.
It obtains module 104 to be used for according to the incidence relation, target data is obtained by the hobby label in auxiliary data source
The hobby label in source.
Result-generation module 105 is used to generate recommendation results according to the hobby label of the target data source.
It should be noted that Collaborative Filtering Recommendation System provided in an embodiment of the present invention, as with side shown in Fig. 1 of the present invention
Method embodiment is based on same design, and bring technical effect is identical as embodiment of the method shown in Fig. 1 of the present invention, and particular content can
Referring to the narration in embodiment of the method shown in Fig. 1 of the present invention, details are not described herein again.
Embodiment six
As shown in fig. 6, in one embodiment of the invention, the screening module 101 in embodiment corresponding to Fig. 5 includes
For executing the structure of the method and step in embodiment corresponding to Fig. 2 comprising:
Socialized label acquiring unit 201 is used to obtain the socialized label of at least one data source.
Screening unit 202 is used to sieve the socialized label of at least one data source by default screening conditions
Choosing is to obtain effective label.
Embodiment seven
As shown in fig. 7, in one embodiment of the invention, the screening module 104 in embodiment corresponding to Fig. 5 includes
For executing the structure of the method and step in embodiment corresponding to Fig. 3 comprising:
Computing unit 301 is used to calculate the pre- of target data source according to the hobby label in auxiliary data source based on incidence relation
Estimate hobby label.
Acquiring unit 302 is selected to like label as institute for estimating for the target data source of predetermined selection condition will to be met
State the hobby label of target data source.
Embodiment eight
As shown in fig. 7, in one embodiment of the invention, the result-generation module 105 in embodiment corresponding to Fig. 5
Including the structure for executing the method and step in embodiment corresponding to Fig. 4 comprising:
Collaborative filtering unit 401 is for being filtered target data source by collaborative filtering.
Label filter element 402 is used to for the product for not including target data source hobby label being filtered to obtain
Recommendation results.
Recommendation unit 403 is used to recommend the recommendation results for meeting default recommendation condition to user.
Embodiment nine:
Fig. 9 is the schematic diagram for the terminal device that one embodiment of the invention provides.As shown in figure 9, the terminal of the embodiment is set
Standby 9 include: processor 90, memory 91 and are stored in the meter that can be run in the memory 91 and on the processor 90
Calculation machine program 92, such as program.The processor 90 realizes above-mentioned each video search side when executing the computer program 92
Step in method embodiment, such as step S101 to S105 shown in FIG. 1.Alternatively, the processor 90 executes the computer
The function of each module/unit in above-mentioned video searching system embodiment, such as module 101 to 105 shown in Fig. 5 are realized when program 92
Function.
Illustratively, the computer program 92 can be divided into one or more module/units, it is one or
Multiple module/units are stored in the memory 91, and are executed by the processor 90, to complete the present invention.Described one
A or multiple module/units can be the series of computation machine program instruction section that can complete specific function, which is used for
Implementation procedure of the computer program 92 in the terminal device 9 is described.For example, the computer program 92 can be divided
It is cut into screening module, analysis module, relationship to establish module, obtain module and result-generation module, each module concrete function is such as
Under:
Screening module filters out having at least one data source for pre-processing at least one data source
Criterion label, at least one described data source include auxiliary data source；
Analysis module, for obtaining the behavioural characteristic of user according to effective label of all data sources, according to the user
Behavioural characteristic analyze the hobby label in all auxiliary data sources；
Relationship establishes module, for by all auxiliary data sources hobby label and target data source hobby label
Establish incidence relation；
Module is obtained, for obtaining target data source by the hobby label in auxiliary data source according to the incidence relation
Hobby label；
Result-generation module, for generating recommendation results according to the hobby label of the target data source.
The terminal device 9 can be the calculating such as desktop PC, notebook, palm PC and cloud server and set
It is standby.The terminal device may include, but be not limited only to, processor 90, memory 91.It will be understood by those skilled in the art that Fig. 9
The only example of terminal device 9 does not constitute the restriction to terminal device 9, may include than illustrating more or fewer portions
Part perhaps combines certain components or different components, such as the terminal device can also include input-output equipment, net
Network access device, bus etc..
Alleged processor 90 can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng.
The memory 91 can be the internal storage unit of the terminal device 9, such as the hard disk or interior of terminal device 9
It deposits.The memory 91 is also possible to the External memory equipment of the terminal device 9, such as be equipped on the terminal device 9
Plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card dodge
Deposit card (Flash Card) etc..Further, the memory 91 can also both include the storage inside list of the terminal device 9
Member also includes External memory equipment.The memory 91 is for storing needed for the computer program and the terminal device
Other programs and data.The memory 91 can be also used for temporarily storing the data that has exported or will export.
It is apparent to those skilled in the art that for convenience of description and succinctly, only with above-mentioned each function
Can unit, module division progress for example, in practical application, can according to need and by above-mentioned function distribution by different
Functional unit, module are completed, i.e., the internal structure of described device is divided into different functional unit or module, more than completing
The all or part of function of description.Each functional unit in embodiment, module can integrate in one processing unit, can also
To be that each unit physically exists alone, can also be integrated in one unit with two or more units, it is above-mentioned integrated
Unit both can take the form of hardware realization, can also realize in the form of software functional units.In addition, each function list
Member, the specific name of module are also only for convenience of distinguishing each other, the protection scope being not intended to limit this application.Above system
The specific work process of middle unit, module, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In the above-described embodiments, it all emphasizes particularly on different fields to the description of each embodiment, is not described in detail or remembers in some embodiment
The part of load may refer to the associated description of other embodiments.
Those of ordinary skill in the art may be aware that list described in conjunction with the examples disclosed in the embodiments of the present disclosure
Member and algorithm steps can be realized with the combination of electronic hardware or computer software and electronic hardware.These functions are actually
It is implemented in hardware or software, the specific application and design constraint depending on technical solution.Professional technician
Each specific application can be used different methods to achieve the described function, but this realization is it is not considered that exceed
The scope of the present invention.
In embodiment provided by the present invention, it should be understood that disclosed device/terminal device and method, it can be with
It realizes by another way.For example, device described above/terminal device embodiment is only schematical, for example, institute
The division of module or unit is stated, only a kind of logical function partition, there may be another division manner in actual implementation, such as
Multiple units or components can be combined or can be integrated into another system, or some features can be ignored or not executed.Separately
A bit, shown or discussed mutual coupling or direct-coupling or communication connection can be through some interfaces, device
Or the INDIRECT COUPLING or communication connection of unit, it can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated module/unit be realized in the form of SFU software functional unit and as independent product sale or
In use, can store in a computer readable storage medium.Based on this understanding, the present invention realizes above-mentioned implementation
All or part of the process in example method, can also instruct relevant hardware to complete, the meter by computer program
Calculation machine program can be stored in a computer readable storage medium, the computer program when being executed by processor, it can be achieved that on
The step of stating each embodiment of the method.Wherein, the computer program includes computer program code, the computer program
Code can be source code form, object identification code form, executable file or certain intermediate forms etc..Computer-readable Jie
Matter may include: can carry the computer program code any entity or device, recording medium, USB flash disk, mobile hard disk,
Magnetic disk, CD, computer storage, read-only memory (ROM, Read-Only Memory), random access memory (RAM,
Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It should be noted that described
The content that computer-readable medium includes can carry out increasing appropriate according to the requirement made laws in jurisdiction with patent practice
Subtract, such as in certain jurisdictions, according to legislation and patent practice, computer-readable medium do not include be electric carrier signal and
Telecommunication signal.
Embodiment described above is merely illustrative of the technical solution of the present invention, rather than its limitations；Although referring to aforementioned reality
Applying example, invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each
Technical solution documented by embodiment is modified or equivalent replacement of some of the technical features；And these are modified
Or replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution should all
It is included within protection scope of the present invention.
Claims (10)
1. a kind of collaborative filtering recommending method, which is characterized in that the described method includes:
At least one data source is pre-processed, filters out effective label of at least one data source, described at least one
A data source includes auxiliary data source；
The behavioural characteristic that user is obtained according to effective label of all data sources is analyzed all according to the behavioural characteristic of the user
The hobby label in auxiliary data source；
The hobby label of the hobby label in all auxiliary data sources and target data source is established into incidence relation；
According to the incidence relation, the hobby label of target data source is obtained by the hobby label in auxiliary data source；
Recommendation results are generated according to the hobby label of the target data source.
2. filtering out institute the method according to claim 1, wherein pre-processing at least one data source
Effective label of at least one data source is stated, at least one described data source includes auxiliary data source, comprising:
Obtain the socialized label of at least one data source；
The socialized label of at least one data source is screened by default screening conditions to obtain effective label.
3. the method according to claim 1, wherein the hobby label by all auxiliary data sources with
The hobby label of target data source establishes incidence relation specifically:
By FP-Growth association rule algorithm excavate all auxiliary data sources hobby label and the target data
Correlation rule between the hobby label in source, establishes incidence relation according to the correlation rule.
4. passing through auxiliary data source the method according to claim 1, wherein described according to the incidence relation
Hobby label obtain target data source hobby label, comprising:
Hobby label is estimated according to the hobby label in auxiliary data source calculating target data source based on incidence relation；
The target data source for meeting predetermined selection condition is estimated into hobby label as the hobby label of the target data source.
5. the method according to claim 1, wherein described generate according to the hobby label of the target data source
Recommendation results, comprising:
Target data source is filtered by collaborative filtering；
The product for not including target data source hobby label is filtered to obtain recommendation results；
The recommendation results for meeting default recommendation condition are recommended to user.
6. a kind of Collaborative Filtering Recommendation System, which is characterized in that the system comprises:
Screening module, for pre-processing at least one data source, filter out at least one data source has criterion
Label, at least one described data source includes auxiliary data source；
Analysis module, for obtaining the behavioural characteristic of user according to effective label of all data sources, according to the row of the user
It is characterized the hobby label for analyzing all auxiliary data sources；
Relationship establishes module, for establishing the hobby label of the hobby label in all auxiliary data sources and target data source
Incidence relation；
Module is obtained, for obtaining the happiness of target data source by the hobby label in auxiliary data source according to the incidence relation
Good label；
Result-generation module, for generating recommendation results according to the hobby label of the target data source.
7. system according to claim 6, which is characterized in that the screening module includes:
Socialized label acquiring unit, for obtaining the socialized label of at least one data source；
Screening unit, for being screened the socialized label of at least one data source to obtain by default screening conditions
Take effective label.
8. system according to claim 6, which is characterized in that the acquisition module includes:
Computing unit, for estimating hobby according to the hobby label in auxiliary data source calculating target data source based on incidence relation
Label；
Acquiring unit is selected, likes label as the target for estimating for the target data source of predetermined selection condition will to be met
The hobby label of data source.
9. a kind of terminal device, including memory, processor and storage are in the memory and can be on the processor
The computer program of operation, which is characterized in that the processor realizes such as claim 1 to 5 when executing the computer program
The step of any one the method.
10. a kind of computer readable storage medium, the computer-readable recording medium storage has computer program, and feature exists
In when the computer program is executed by processor the step of any one of such as claim 1 to 5 of realization the method.
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| CN110442791A (en) * | 2019-08-08 | 2019-11-12 | 北京阿尔山区块链联盟科技有限公司 | Data push method and system |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110442791A (en) * | 2019-08-08 | 2019-11-12 | 北京阿尔山区块链联盟科技有限公司 | Data push method and system |
| CN111259213A (en) * | 2020-01-07 | 2020-06-09 | 中国联合网络通信集团有限公司 | Data visualization processing method and device |
| CN111522993A (en) * | 2020-04-08 | 2020-08-11 | 咪咕文化科技有限公司 | Label validity management method and device, network equipment and storage medium |
| CN111522993B (en) * | 2020-04-08 | 2023-08-15 | 咪咕文化科技有限公司 | Tag validity management method, device, network equipment and storage medium |
| CN113177643A (en) * | 2021-05-24 | 2021-07-27 | 北京融七牛信息技术有限公司 | Automatic modeling system based on big data |
| CN113177642A (en) * | 2021-05-24 | 2021-07-27 | 北京融七牛信息技术有限公司 | Automatic modeling system for data imbalance |
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