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Title
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Top-N recommendation for shared accounts
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Author
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Abstract
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| Standard collaborative filtering recommender systems assume that every account in the training data represents a single user. However, multiple users often share a single account. A typical example is a single shopping account for the whole family. Traditional recommender systems fail in this situation. If contextual information is available, context aware recommender systems are the state-of-the-art solution. Yet, often no contextual information is available. Therefore, we introduce the challenge of recommending to shared accounts in the absence of contextual information. We propose a solution to this challenge for all cases in which the reference recommender system is an item-based top-N collaborative filtering recommender system, generating recommendations based on binary, positive-only feedback. We experimentally show the advantages of our proposed solution for tackling the problems that arise from the existence of shared accounts on multiple datasets. |
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Language
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English
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Source (book)
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Proceedings of the 9th ACM Conference on Recommender Systems
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Publication
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S.l.
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ACM
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2015
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DOI
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10.1145/2792838.2800170
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Volume/pages
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p. 59-66
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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