NDLI: Improving the Recommendation of Collaborative Filtering by Fusing Trust Network

Content Provider	IEEE Xplore Digital Library
Author	Bo Yang Pengfei Zhao Shuqiu Ping Jing Huang
Copyright Year	2012
Abstract	To accurately and actively provide users with their potentially interested information or services is the main task of a recommender system. Collaborative filtering is one of the most widely adopted recommender methods, whereas it is suffering the issue of sparse rating data that will severely degenerate the quality of recommendations. To address this issue, the article proposes a novel method, named the FTRA (Fusing Trust and Ratings), trying to improve the performance of collaborative filtering recommendation by means of elaborately integrating twofold sparse information, i.e., the conventional rating data given by users and the social trust network among the same users. The performance of FTRA is rigorously validated by comparing it with six representative methods on a real-world dataset. The experimental results show that the FTRA outperforms all other competitors in terms of both precision and recall. More importantly, our work suggests that the strategy of augmenting sparse rating data by fusing trust networks does significantly improve the quality of conventional collaborative filtering recommendation, and its quality could be further improved by means of designing more effective integrating schemes.
Starting Page	195
Ending Page	199
File Size	162069
Page Count	5
File Format	PDF
ISBN	9781467347259
DOI	10.1109/CIS.2012.51
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement collaborative filtering trust network Social network services graph theory similarity analysis Collaboration Educational institutions Web sites Recommender systems
Content Type	Text
Resource Type	Article

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