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.
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Kabutoya, Y., Iwata, T., Fujimura, K. (2010). Modeling Multiple Users’ Purchase over a Single Account for Collaborative Filtering. In: Chen, L., Triantafillou, P., Suel, T. (eds) Web Information Systems Engineering – WISE 2010. WISE 2010. Lecture Notes in Computer Science, vol 6488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17616-6_30
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DOI: https://doi.org/10.1007/978-3-642-17616-6_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17615-9
Online ISBN: 978-3-642-17616-6
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