Modeling multiple users' purchase over a single account for collaborative filtering

Authors:
Yutaka Kabutoya;Tomoharu Iwata;Ko Fujimura
Affiliations:
NTT Cyber Solutions Laboratories, NTT Corporation, Yokosuka-shi, Kanagawa, Japan;NTT Communication Science Laboratories, NTT Corporation, Kyoto, Japan;NTT Cyber Solutions Laboratories, NTT Corporation, Yokosuka-shi, Kanagawa, Japan
Venue:
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Year:
2010

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Abstract

We propose a probabilistic topic model for enhancing recommender systems to handle multiple users that share a single account. In several web services, since multiple individuals may share one account (e.g. a family), individual preferences cannot be estimated from a simple perusal of the purchase history of the account, thus it is difficult to accurately recommend items to those who share an account. We tackle this problem by assuming latent users sharing an account and establish a model by extending Probabilistic Latent Semantic Analysis (PLSA). Experiments on real log datasets from online movie services and artificial datasets created from these real datasets by combining the purchase histories of two accounts demonstrate high prediction accuracy of users and higher recommendation accuracy than conventional methods.