CN104216884B - Collaborative filtering system and method based on time attenuation - Google Patents
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Abstract
The invention discloses a collaborative filtering method and a collaborative filtering system based on time attenuation, wherein the method comprises the following steps: decomposing the frequency and the co-occurrence in the traditional collaborative filtering algorithm formula to form a new collaborative filtering formula; introducing a time attenuation factor into the new collaborative filtering formula, and constructing a final collaborative filtering formula, so that data which are generated recently by user behaviors are mostly adopted in an application scene with faster user interest migration, and data with slower user interest behaviors change are mostly utilized by using historical behavior records of users; according to the invention, the corresponding time attenuation factor is selected to filter according to specific needs to complete recommendation, and the invention can flexibly select the range of user behavior data by adjusting the time attenuation factor, quickly adjust to adapt to specific application, accurately analyze the interest of the user and better realize recommendation.
Description
Technical Field
The present invention relates to a collaborative filtering system and method, and more particularly, to a collaborative filtering system and method based on time attenuation.
Background
The electronic commerce website or the content website has a large amount of goods or contents, a user does not know all the contents of the website, the user searches for needed things by browsing and searching through a directory page, and the application of the current recommendation system is more and more extensive and gradually becomes a good way for finding the user by the contents.
Collaborative filtering is a common algorithm for recommendation systems. The collaborative filtering is that according to the historical behavior record of the user, a certain commodity is usually browsed, the certain commodity is collected and purchased, the relation between the commodity and the commodity can be found according to the historical behavior record, when a new user browses the commodity next time, the commodity required by the new user is recommended to the new user, and the commodity required by the new user is often recommended to the new user, so that the user is actively helped.
The traditional collaborative filtering algorithm calculates the degree of association between two commodities by using the following formula:
Relate(A，B)＝CoOccur(A，B)/(Freq(A)*Freq(B))。
namely: the incidence relation of A and B is in direct proportion to the simultaneous occurrence times of A and B and in inverse proportion to the independent occurrence times of A and B.
The recommendation system firstly needs to consider the interest of the user, and what the user is interested in can be expressed by the browsing behavior of the user on the commodity, and the content of the website is recommended to the user according to the interest of the user, so that the probability of successful recommendation is often improved.
However, the logs of user behavior on which recommendations are based typically occupy a large amount of storage space. The larger the amount of logging, the longer the time of calculation. Through analyzing massive logs, the interest of the user is found, and the content of interest of the user is recommended to the user. While the user behavior logs are continuously increasing, the interests of the users change with time. How to utilize massive logs can quickly and accurately analyze the interest of the user. This becomes a problem that has to be solved in the actual system development.
In addition, the collaborative filtering algorithm can be applied to a plurality of scenes, and can be a relatively universal algorithm for shopping websites and content websites. However, in a specific website, according to different applications, different contents, different users, and algorithms often need to be adjusted according to specific situations. In some applications, the user demand changes rapidly, and how the collaborative filtering algorithm can be adjusted rapidly to adapt to specific applications is also a problem to be solved.
Disclosure of Invention
In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a collaborative filtering system and method based on time decay, which can adjust the time decay factor according to specific situations, flexibly select the range of user behavior data, quickly adjust to adapt to specific applications, and accurately analyze the interests of users.
To achieve the above and other objects, the present invention provides a collaborative filtering method based on time decay, comprising the steps of:
the method comprises the following steps that firstly, frequency and co-occurrence in a traditional collaborative filtering algorithm formula are decomposed to form a new collaborative filtering formula;
introducing a time attenuation factor into the new collaborative filtering formula to construct a final collaborative filtering formula, so that data which are generated recently by user behaviors are mostly adopted in an application scene with faster user interest migration, and data with slower user interest behavior change are mostly utilized by using historical behavior records of users;
and step three, selecting a corresponding time attenuation factor according to specific needs to filter, and completing recommendation.
Further, in the first step, the frequency is decomposed into an increment frequency and a stock frequency, and the co-occurrence is decomposed into an increment co-occurrence and a stock co-occurrence.
Further, the value of the time attenuation factor is between 0 and 1.
Further, when the value of the time attenuation factor is 0, the newly obtained user behavior data is completely utilized as the basis for recommendation. And when the time attenuation factor value is 1, all data from the date of log storage is used as a recommendation basis.
Further, in step three, a time attenuation factor is selected according to the frequency and the magnitude of the co-occurrence.
Further, the final collaborative filtering formula is not attenuated after being attenuated to a threshold value, so that historical information is not lost.
To achieve the above and other objects, the present invention further provides a collaborative filtering system based on time decay, comprising at least:
the decomposition module is used for decomposing the frequency and the co-occurrence in the traditional collaborative filtering algorithm formula to form a new collaborative filtering formula;
the time attenuation factor introducing module introduces a time attenuation factor into the new collaborative filtering formula to construct a final collaborative filtering formula, so that the application scene with faster user interest migration mostly adopts data which are generated recently by user behaviors, and the data with slower user interest behavior change mostly utilizes historical behavior records of the user;
and the time attenuation factor selection module selects corresponding time attenuation factors for collaborative filtering according to specific requirements to complete recommendation.
Further, the decomposition module decomposes the frequency into an incremental frequency and a stock frequency, and the co-occurrence is decomposed into an incremental co-occurrence and a stock co-occurrence.
Further, the value of the time attenuation factor is between 0 and 1.
Further, the time attenuation factor selection module selects the time attenuation factor according to the frequency and the co-occurrence size.
Compared with the prior art, the collaborative filtering system and the collaborative filtering method based on the time attenuation are characterized in that a new collaborative filtering formula is formed by decomposing the frequency and the co-occurrence in the traditional collaborative filtering algorithm formula, and the time attenuation factor is introduced into the new collaborative filtering formula so as to select the time attenuation factor according to the requirement, so that the time attenuation factor can be adjusted according to specific conditions, the range of user behavior data is flexibly selected, the range of the user behavior data is rapidly adjusted to adapt to specific application, the interest of a user is more accurately analyzed, and better recommendation is realized.
Drawings
FIG. 1 is a flow chart illustrating the steps of a collaborative filtering method based on time decay according to the present invention;
FIG. 2 is a graph of the decay period for a set decay factor of 0.9;
fig. 3 is a system architecture diagram of a collaborative filtering system based on time decay in accordance with the present invention.
Detailed Description
Other advantages and capabilities of the present invention will be readily apparent to those skilled in the art from the present disclosure by describing the embodiments of the present invention with specific embodiments thereof in conjunction with the accompanying drawings. The invention is capable of other and different embodiments and its several details are capable of modification in various other respects, all without departing from the spirit and scope of the present invention.
FIG. 1 is a flow chart illustrating the steps of a collaborative filtering method based on time decay according to the present invention. As shown in fig. 1, the collaborative filtering method based on time decay of the present invention includes the following steps:
Namely: CoOccur (a, B) ═ His _ CoOccur (a, B) + New _ CoOccur (a, B)
Freq(A)＝His_Freq(A)+New_Freq(A)
Freq(B)＝His_Freq(B)+New_Freq(B)。
Thus, the conventional collaborative filtering algorithm becomes:
Relate(A，B)＝(His_CoOccur(A，B)+New_CoOccur(A，B))/((His_Freq(A)+New_Freq(A))*(His_Freq(B)+New_Freq(B)))。
and 102, introducing a time attenuation factor into the new collaborative filtering formula, and constructing a final collaborative filtering formula, so that the application scene with faster user interest migration has multiple functions of data which are generated recently by user behaviors, and the application scene with slower user interest behavior change has multiple functions of data which are recorded by the historical behaviors of the user.
Here, the time factor is expressed by Alfa.
The final collaborative filtering formula can be expressed by the following formula.
Relate(A，B)＝(Alfa*His_CoOccur(A，B)+New_CoOccur(A，B))/((Alfa*His_Freq(A)+New_Freq(A))*(Alfa*His_Freq(B)+New_Freq(B)))。
Wherein the Alfa value is 0-1.
In an extreme case, when the value of Alfa is 0, the newly obtained user behavior data is completely utilized as the basis for recommendation. When Alfa is 1, all data from the date of log storage is used as the basis for recommendation.
And 103, selecting corresponding time attenuation factors for collaborative filtering according to specific requirements, and completing recommendation. Generally, the time attenuation factor is selected according to the frequency and the size of the co-occurrence, and in order to avoid losing the historical information, the attenuation preferably has a loss stopping value, that is, the final collaborative filtering formula is not attenuated after being attenuated to a certain threshold value, so that the loss of the historical information can be avoided. Fig. 2 is a graph of the decay period when the decay factor is set to 0.9, and it can be seen that the time decay factor can flexibly and effectively adjust the weight of the user interest distribution over time.
Fig. 3 is a system architecture diagram of a collaborative filtering system based on time decay in accordance with the present invention. As shown in fig. 3, the collaborative filtering system based on time decay of the present invention at least comprises: a decomposition module 301, a time decay factor introducing module 302 and a time decay factor selecting module 303.
The decomposition module 301 is configured to decompose the frequencies freq (a), freq (B), and CoOccur (a, B) in the conventional collaborative filtering algorithm formula to form a new collaborative filtering formula. The frequency is decomposed into increment frequency and stock frequency, and the co-occurrence is decomposed into increment co-occurrence and stock co-occurrence.
Namely: CoOccur (a, B) ═ His _ CoOccur (a, B) + New _ CoOccur (a, B)
Freq(A)＝His_Freq(A)+New_Freq(A)
Freq(B)＝His_Freq(B)+New_Freq(B)。
Thus, the new collaborative filtering formula becomes:
Relate(A，B)＝(His_CoOccur(A，B)+New_CoOccur(A，B))/((His_Freq(A)+New_Freq(A))*(His_Freq(B)+New_Freq(B)))。
the time decay factor introducing module 302 introduces a time decay factor into the new collaborative filtering formula to construct a final collaborative filtering formula, so that the application scene with faster user interest migration uses the data which is generated recently by the user behavior, and the data with slower user interest behavior change uses the historical behavior record of the user.
Here, the time factor is expressed by Alfa.
The final collaborative filtering formula can be expressed by the following formula.
Relate(A，B)＝(Alfa*His_CoOccur(A，B)+New_CoOccur(A，B))/((Alfa*His_Freq(A)+New_Freq(A))*(Alfa*His_Freq(B)+New_Freq(B)))。
Wherein the Alfa value is 0-1.
In an extreme case, when the value of Alfa is 0, the newly obtained user behavior data is completely utilized as the basis for recommendation. When Alfa is 1, all data from the date of log storage is used as the basis for recommendation.
The time attenuation factor selection module 303 selects a corresponding time attenuation factor according to specific requirements. Generally, it is desirable to select the time attenuation factor according to the frequency and the size of the co-occurrence, and to avoid losing historical information, the attenuation preferably has a stop loss value, i.e. the attenuation is not reduced after a certain amount, so that the loss of historical information can be avoided
In summary, the collaborative filtering system and method based on time attenuation of the present invention decompose the frequency and co-occurrence in the traditional collaborative filtering algorithm formula to form a new collaborative filtering formula, and introduce a time attenuation factor into the new collaborative filtering formula, so as to select the time attenuation factor according to the requirement, so that the present invention can adjust the time attenuation factor according to the specific situation, flexibly select the range of the user behavior data, rapidly adjust the range of the user behavior data to adapt to the specific application, more accurately analyze the user interest, and realize better recommendation.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Modifications and variations can be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the present invention. Therefore, the scope of the invention should be determined from the following claims.
Claims (5)
1. A collaborative filtering method based on time decay comprises the following steps:
the first step, frequency freq (A), freq (B) and co-occurrence CoOccur (A, B) in the traditional collaborative filtering algorithm formula are decomposed to form a collaborative filtering formula, wherein the frequency is decomposed into increment frequency and stock frequency, the co-occurrence is decomposed into increment co-occurrence and stock co-occurrence, and the collaborative filtering formula is as follows:
Relate(A，B)＝(His_CoOccur(A，B)+New_CoOccur(A，B))/((His_Freq(A)+New_Freq(A))*(His_Freq(B)+New_Freq(B)))；
step two, introducing a time attenuation factor Alfa into the collaborative filtering formula to construct a final collaborative filtering formula, so that the application scene with faster user interest migration mostly adopts data which are generated recently by user behaviors, and the data with slower user interest behavior change mostly utilizes historical behavior records of the user, and the final collaborative filtering formula is as follows:
Relate(A，B)＝(Alfa*His_CoOccur(A，B)+New_CoOccur(A，B))/((Alfa*His_Freq(A)+New_Freq(A))*(Alfa*His_Freq(B)+New_Freq(B)))；
and step three, selecting a corresponding time attenuation factor according to specific needs to filter to finish recommendation, wherein the value of the time attenuation factor Alfa is more than 0 and less than 1.
2. The collaborative filtering method based on time decay according to claim 1, wherein: in step three, the time attenuation factor is selected according to the frequency and the size of the co-occurrence.
3. The collaborative filtering method based on time decay according to claim 1, wherein: the final collaborative filtering formula is not attenuated after being attenuated to a threshold value so as to avoid losing historical information.
4. A collaborative filtering system based on temporal decay, comprising at least:
the decomposition module is used for decomposing the frequencies freq (A), freq (B) and the co-occurrence CoOccur (A, B) in the traditional collaborative filtering algorithm formula to form a collaborative filtering formula, wherein the frequency is decomposed into increment frequency and stock frequency, the co-occurrence is decomposed into increment co-occurrence and stock co-occurrence, and the collaborative filtering formula is as follows:
Relate(A，B)＝(His_CoOccur(A，B)+New_CoOccur(A，B))/((His_Freq(A)+New_Freq(A))*(His_Freq(B)+New_Freq(B)))；
the time attenuation factor introducing module introduces a time attenuation factor Alfa into the collaborative filtering formula to construct a final collaborative filtering formula, so that the application scene with faster user interest migration mostly adopts data which are generated recently by user behaviors, and the data with slower user interest behavior change mostly utilizes historical behavior records of users, and the final collaborative filtering formula is as follows:
Relate(A，B)＝(Alfa*His_CoOccur(A，B)+New_CoOccur(A，B))/((Alfa*His_Freq(A)+New_Freq(A))*(Alfa*His_Freq(B)+New_Freq(B)))；
and the time attenuation factor selection module selects a corresponding time attenuation factor to perform collaborative filtering according to specific requirements to complete recommendation, wherein the value of the time attenuation factor Alfa is more than 0 and less than 1.
5. The collaborative filtering system based on time decay according to claim 4, wherein: the time attenuation factor selection module selects a time attenuation factor according to the frequency and the magnitude of the co-occurrence.
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| CN105930507B (en) * | 2016-05-10 | 2019-11-29 | 腾讯科技（深圳）有限公司 | A kind of method and device for the web browsing interest obtaining user |
| CN106709755A (en) * | 2016-11-28 | 2017-05-24 | 加和（北京）信息科技有限公司 | Method of predicting user frequency and apparatus thereof |
| CN107343209A (en) * | 2017-06-29 | 2017-11-10 | 达而观信息科技（上海）有限公司 | A kind of UGC associated video generation methods based on increment collaborative filtering |
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| CN111339434B (en) * | 2018-12-03 | 2023-04-28 | 阿里巴巴集团控股有限公司 | Information recommendation method and device, electronic equipment and computer storage medium |
| CN112052378B (en) * | 2019-10-15 | 2021-09-21 | 河南紫联物联网技术有限公司 | Intelligent terminal, and recommendation method and system for intelligent home application |
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