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Title
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Unifying nearest neighbors collaborative filtering
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Author
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Abstract
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| We study collaborative filtering for applications in which there exists for every user a set of items about which the user has given binary, positive-only feedback (one-class collaborative filtering). Take for example an on-line store that knows all past purchases of every customer. An important class of algorithms for one-class collaborative filtering are the nearest neighbors algorithms, typically divided into user-based and item-based algorithms. We introduce a reformulation that unifies user- and item-based nearest neighbors algorithms and use this reformulation to propose a novel algorithm that incorporates the best of both worlds and outperforms state-of-the-art algorithms. Additionally, we propose a method for naturally explaining the recommendations made by our algorithm and show that this method is also applicable to existing user-based nearest neighbors methods. |
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Language
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English
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Source (book)
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Proceedings of the 8th ACM Conference on Recommender Systems
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Publication
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New York, N.Y.
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ACM
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2014
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ISBN
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978-1-4503-2668-1
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DOI
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10.1145/2645710.2645731
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Volume/pages
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p. 177-184
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Full text (Publisher's DOI)
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