TY - GEN
T1 - Augmenting collaborative recommender by fusing explicit social relationships
AU - Yuan, Quan
AU - Zhao, Shiwan
AU - CHEN, Li
AU - Liu, Yan
AU - Ding, Shengchao
AU - Zhang, Xiatian
AU - Zheng, Wentao
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2009/10
Y1 - 2009/10
N2 - 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.
AB - 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.
KW - Collaborative filtering
KW - Random walk
KW - Recommender systems
KW - Social relationship
UR - http://www.scopus.com/inward/record.url?scp=84888384369&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84888384369
SN - 9781605584355
T3 - CEUR Workshop Proceedings
SP - 49
EP - 56
BT - Proceedings of the ACM RecSys'09 Workshop on Recommender Systems & the Social Web
T2 - Workshop on Recommender Systems and the Social Web, Collocated with the 3rd ACM Conference on Recommender Systems, ACM RecSys 2009
Y2 - 25 October 2009 through 25 October 2009
ER -