A service recommendation method based on outlier user filtering

A service recommendation and user technology, applied in the computer field, can solve problems such as irrelevant tags and abnormal behavior

Active Publication Date: 2021-09-14
HUBEI UNIV
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Problems solved by technology

[0004] The present invention is mainly aimed at untrustworthy problems such as abnormal behavior and irrelevant labels in the process of labeling services by users. In order to ensure the quality of user data in the process of service recommendation, from the perspective of clustering, a two-stage unsupervised The K-means algorithm proposed a service recommendation method based on outlier user filtering, so as to improve the quality of service recommendation

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  • A service recommendation method based on outlier user filtering
  • A service recommendation method based on outlier user filtering
  • A service recommendation method based on outlier user filtering

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Embodiment Construction

[0035] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0036] please see figure 1 , a service recommendation method based on outlier user filtering provided by the present invention, firstly filter out user sets with abnormal tagging behaviors through two-stage K-means clustering; then perform service collaboration for target users based on the remaining user sets recommend.

[0037] Table 1 is a sample data set with 5 users and 4 services, l i,j is the jth label marked by the user on service i, and the corresponding attention matrix is ​​shown in Table 2. It is worth noting that not all users will pay attenti...

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Abstract

The invention discloses a service recommendation method based on outlier user filtering. Firstly, the user-service-label information in two representative data sets Last.FM and Delicious provided by the Grouplens platform is obtained, and for each service, the All the users who have paid attention to it (expressed as labeling the service) are clustered, and the users in the smallest group after clustering are regarded as candidate untrustworthy users; then based on the number of times the user is marked as candidate untrustworthy The users are clustered for the second time, so that the users in the group with too many untrustworthy candidates are regarded as the final untrustworthy user set; finally, the top-k similar users are recommended for the target user in the credible user set, and according to Similar user preferences for service recommendation. The invention can realize service recommendation with high recommendation precision, and solves the problem that untrustworthy users mislead the recommendation results.

Description

technical field [0001] The invention belongs to the field of computer technology and relates to a service recommendation method in the field of intelligent service computing, in particular to a recommendation method based on two-stage k-means clustering and collaborative filtering. Background technique [0002] The rapid development of the Internet has led to an increasing number of web services, making it more and more complicated for users to choose the services they actually need. The recommendation system can effectively help users deal with the problem of information load, so as to quickly find the service that suits them, and even provide personalized recommendation services according to the characteristics of users. Typical service recommendation applications include product recommendation, movie recommendation, book recommendation, and so on. [0003] Collaborative filtering is one of the commonly used effective methods in service recommendation, which mainly makes ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06K9/62
CPCG06F18/23213
Inventor 常志远吴浩周寅莹张涵钰何鹏
Owner HUBEI UNIV
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