NDLI: Graph-Based Recommendation on Social Networks

Content Provider	IEEE Xplore Digital Library
Author	Ziqi Wang Yuwei Tan Ming Zhang
Copyright Year	2010
Abstract	Recommender systems have emerged as an essential response to the rapidly growing digital information phenomenon in which users are finding it more and more difficult to locate the right information at the right time. Systems under Web2.0 allow users not only to give resources- ratings but also to assign tags to them. Tags play a significant role in Web 2.0. They can be used for navigation, browsing, recommendation and so on. In this paper, we propose a novel recommendation algorithm, which is based on social networks. The social network is established among users and items, taking into account both the information of ratings and tags. We consider users’ co-tagging behaviors and add the similarity relationship to the graph to enhance the performance. Our algorithm is based on the Random Walk with Restarts but provides a more natural and efficient way to represent social networks. Having considered the influence of tags, the transition matrix is denser and the recommendation is more accurate. By evaluating our new algorithm and comparing it to the baseline algorithm which is used in many real world recommender systems on a real life dataset, we make the conclusion that our method performs better than the baseline method.
Starting Page	116
Ending Page	122
File Size	298076
Page Count	7
File Format	PDF
ISBN	9781769540122
e-ISBN	9781424466009
DOI	10.1109/APWeb.2010.60
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-06
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Social Networks Social network services Graph-based Algorithm Collaboration Tagging Motion pictures Random Walk with Restarts Recommender systems Recommendation
Content Type	Text
Resource Type	Article

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