TY - GEN
T1 - Recommendation as link prediction
T2 - 2009 ACM/IEEE Joint Conference on Digital Libraries, JCDL'09
AU - Li, Xin
AU - Chen, Hsinchun
PY - 2009/1/1
Y1 - 2009/1/1
N2 - Recommender systems have demonstrated commercial success in multiple industries. In digital libraries they have the potential to be used as a support tool for traditional information retrieval functions. Among the major recommendation algorithms, the successful collaborative filtering (CF) methods explore the use of user-item interactions to infer user interests. Based on the finding that transitive user-item associations can alleviate the data sparsity problem in CF, multiple heuristic algorithms were designed to take advantage of the user-item interaction networks with both direct and indirect interactions. However, the use of such graph representation was still limited in learning-based algorithms. In this paper, we propose a graph kernel-based recommendation framework. For each user-item pair, we inspect its associative interaction graph (AIG) that contains the users, items, and interactions n steps away from the pair. We design a novel graph kernel to capture the AIG structures and use them to predict possible user-item interactions. The framework demonstrates improved performance on an online bookstore dataset, especially when a large number of suggestions are needed.
AB - Recommender systems have demonstrated commercial success in multiple industries. In digital libraries they have the potential to be used as a support tool for traditional information retrieval functions. Among the major recommendation algorithms, the successful collaborative filtering (CF) methods explore the use of user-item interactions to infer user interests. Based on the finding that transitive user-item associations can alleviate the data sparsity problem in CF, multiple heuristic algorithms were designed to take advantage of the user-item interaction networks with both direct and indirect interactions. However, the use of such graph representation was still limited in learning-based algorithms. In this paper, we propose a graph kernel-based recommendation framework. For each user-item pair, we inspect its associative interaction graph (AIG) that contains the users, items, and interactions n steps away from the pair. We design a novel graph kernel to capture the AIG structures and use them to predict possible user-item interactions. The framework demonstrates improved performance on an online bookstore dataset, especially when a large number of suggestions are needed.
KW - Collaborative filtering
KW - Kernel methods
KW - Recommender system
UR - http://www.scopus.com/inward/record.url?scp=70450235024&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70450235024&partnerID=8YFLogxK
U2 - 10.1145/1555400.1555433
DO - 10.1145/1555400.1555433
M3 - Conference contribution
AN - SCOPUS:70450235024
SN - 9781605586977
T3 - Proceedings of the ACM/IEEE Joint Conference on Digital Libraries
SP - 213
EP - 216
BT - JCDL'09 - Proceedings of the 2009 ACM/IEEE Joint Conference on Digital Libraries
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 June 2009 through 19 June 2009
ER -