Abstract
Nowadays social websites have become a major trend in the Web 2.0 environment, enabling abundant social data available. In this paper, we explore the role of two types of social relationships: membership and friendship, while being fused with traditional CF (Collaborative Filtering) recommender methods in order to more accurately predict users' interests and produce recommendations to them. Through an exploratory evaluation with real-life dataset from Last.fm, we have revealed respective effects of the two explicit relationships and furthermore their combinative impacts. In addition, the fusion is conducted via random walk graph model in comparison with via weighted neighborhood similarity matrix, so as to identify the best performance platform. Indepth analysis on the experimental data particularly shows the significant improvement by up to 8% on recommendation accuracy, by embedding social relationships in CF via graph model.
Original language | English |
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Title of host publication | Proceedings of the Workshop on Recommender Systems and the Social Web,collocated with the 3rd ACM Conference on Recommender Systems (RecSys'09) |
Editors | Dietmar Jannach, Werner Geyer, Jill Freyne, Sarabjot Singh Anand, Casey Dugan, Bamshad Mobasher, Alfred Kobsa |
Publisher | CEUR-WS |
Pages | 49-56 |
Number of pages | 8 |
ISBN (Print) | 9781605584355 |
Publication status | Published - Oct 2009 |
Event | Workshop on Recommender Systems and the Social Web, Collocated with the 3rd ACM Conference on Recommender Systems, ACM RecSys 2009 - New York, NY, United States Duration: 25 Oct 2009 → 25 Oct 2009 https://ceur-ws.org/Vol-532/ |
Publication series
Name | CEUR Workshop Proceedings |
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Publisher | CEUR-WS |
Volume | 532 |
ISSN (Print) | 1613-0073 |
Conference
Conference | Workshop on Recommender Systems and the Social Web, Collocated with the 3rd ACM Conference on Recommender Systems, ACM RecSys 2009 |
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Country/Territory | United States |
City | New York, NY |
Period | 25/10/09 → 25/10/09 |
Internet address |
Scopus Subject Areas
- Computer Science(all)
User-Defined Keywords
- Collaborative filtering
- Random walk
- Recommender systems
- Social relationship