TY - CHAP
T1 - Augmenting Collaborative Recommenders by Fusing Social Relationships
T2 - Membership and Friendship
AU - Yuan, Quan
AU - Chen, Li
AU - Zhao, Shiwan
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2012/1/21
Y1 - 2012/1/21
N2 - Collaborative filtering (CF) based recommender systems often suffer from the sparsity problem, particularly for new and inactive users when they use the system. The emerging trend of social networking sites can potentially help alleviate the sparsity problem with their provided social relationship data, by which users' similar interests might be inferred even with few of their behavioral data with items (e.g., ratings). Previous works mainly focus on the friendship and trust relation in this respect. However, in this paper, we have in-depth explored a new kind of social relationship - the membership and its combinational effect with friendship. The social relationships are fused into the CF recommender via a graph-based framework on sparse and dense datasets as obtained from Last.fm. Our experiments have not only revealed the significant effects of the two relationships, especially the membership, in augmenting recommendation accuracy in the sparse data condition, but also identified the outperforming ability of the graph modeling in terms of realizing the optimal fusion mechanism.
AB - Collaborative filtering (CF) based recommender systems often suffer from the sparsity problem, particularly for new and inactive users when they use the system. The emerging trend of social networking sites can potentially help alleviate the sparsity problem with their provided social relationship data, by which users' similar interests might be inferred even with few of their behavioral data with items (e.g., ratings). Previous works mainly focus on the friendship and trust relation in this respect. However, in this paper, we have in-depth explored a new kind of social relationship - the membership and its combinational effect with friendship. The social relationships are fused into the CF recommender via a graph-based framework on sparse and dense datasets as obtained from Last.fm. Our experiments have not only revealed the significant effects of the two relationships, especially the membership, in augmenting recommendation accuracy in the sparse data condition, but also identified the outperforming ability of the graph modeling in terms of realizing the optimal fusion mechanism.
KW - Recommender System
KW - Sparse Data
KW - Collaborative Filter
KW - Random Walk Model
KW - Social Data
UR - http://www.scopus.com/inward/record.url?scp=84885630814&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25694-3_8
DO - 10.1007/978-3-642-25694-3_8
M3 - Chapter
AN - SCOPUS:84885630814
SN - 9783642256936
SN - 9783642446276
T3 - Intelligent Systems Reference Library
SP - 159
EP - 175
BT - Recommender Systems for the Social Web
A2 - Pazos Arias, José J.
A2 - Vilas, Ana Fernández
A2 - Redondo, Rebeca P. Díaz
PB - Springer Berlin Heidelberg
ER -