TY - GEN
T1 - Exploiting endorsement information and social influence for item recommendation
AU - Li, Cheng Te
AU - Lin, Shou De
AU - Shan, Man Kwan
PY - 2011
Y1 - 2011
N2 - Social networking services possess two features: (1) capturing the social relationships among people, represented by the social network, and (2) allowing users to express their preferences on different kinds of items (e.g. photo, celebrity, pages) through endorsing buttons, represented by a kind of endorsement bipartite graph. In this work, using such information, we propose a novel recommendation method, which leverages the viral marketing in the social network and the wisdom of crowds from endorsement network. Our recommendation consists of two parts. First, given some query terms describing user's preference, we find a set of targeted influencers who have the maximum activation probability on those nodes related to the query terms in the social network. Second, based on the derived targeted influencers as key experts, we recommend items via the endorsement network. We conduct the experiments on DBLP co-authorship social network with author-reference data as the endorsement network. The results show our method can achieve effective recommendations.
AB - Social networking services possess two features: (1) capturing the social relationships among people, represented by the social network, and (2) allowing users to express their preferences on different kinds of items (e.g. photo, celebrity, pages) through endorsing buttons, represented by a kind of endorsement bipartite graph. In this work, using such information, we propose a novel recommendation method, which leverages the viral marketing in the social network and the wisdom of crowds from endorsement network. Our recommendation consists of two parts. First, given some query terms describing user's preference, we find a set of targeted influencers who have the maximum activation probability on those nodes related to the query terms in the social network. Second, based on the derived targeted influencers as key experts, we recommend items via the endorsement network. We conduct the experiments on DBLP co-authorship social network with author-reference data as the endorsement network. The results show our method can achieve effective recommendations.
UR - http://www.scopus.com/inward/record.url?scp=80052104076&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052104076&partnerID=8YFLogxK
U2 - 10.1145/2009916.2010084
DO - 10.1145/2009916.2010084
M3 - Conference contribution
AN - SCOPUS:80052104076
SN - 9781450309349
T3 - SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 1131
EP - 1132
BT - SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery
T2 - 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011
Y2 - 24 July 2011 through 28 July 2011
ER -