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- W2014321108 abstract "Most collaborative Recommender Systems (RS) operate in a single domain (such as movies, books, etc.) and are capable of providing recommendations based on historical usage data which is collected in the specific domain only. Cross-domain recommenders address the sparsity problem by using Machine Learning (ML) techniques to transfer knowledge from a dense domain into a sparse target domain. In this paper we propose a transfer learning technique that extracts knowledge from multiple domains containing rich data (e.g., movies and music) and generates recommendations for a sparse target domain (e.g., games). Our method learns the relatedness between the different source domains and the target domain, without requiring overlapping users between domains. The model integrates the appropriate amount of knowledge from each domain in order to enrich the target domain data. Experiments with several datasets reveal that, using multiple sources and the relatedness between domains improves accuracy of results." @default.
- W2014321108 created "2016-06-24" @default.
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- W2014321108 date "2012-10-29" @default.
- W2014321108 modified "2023-09-23" @default.
- W2014321108 title "TALMUD" @default.
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- W2014321108 doi "https://doi.org/10.1145/2396761.2396817" @default.
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