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• W2564282078 abstract "Implicit feedback such as users’ examination behaviors have been recognized as a very important source of information in most recommendation scenarios. For recommendation with implicit feedback, a good similarity measurement and a proper preference assumption are critical for the quality of personalization services. So far, the similarities used in the state-of-the-art recommendation methods include the predefined similarity and the learned similarity; and the preference assumptions include the well-known pairwise assumption. In this paper, we exploit the complementarity of the predefined similarity and the learned similarity via a novel mixed similarity model. Furthermore, we develop a novel recommendation algorithm, i.e., pairwise factored mixed similarity model (P-FMSM), based on the mixed similarity and pairwise preference assumption. Our P-FMSM is able to (i) capture the locality of the user-item interactions via the symmetric predefined similarity, (ii) model the global correlations among items via the asymmetric learned similarity, and (iii) digest the uncertain implicit feedback via the pairwise preference assumption. Empirical studies on four public datasets show that our P-FMSM can recommend significantly more accurate than several state-of-the-art methods." @default.
• W2564282078 created "2017-01-06" @default.
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• W2564282078 date "2017-03-01" @default.
• W2564282078 modified "2023-09-22" @default.
• W2564282078 title "Mixed similarity learning for recommendation with implicit feedback" @default.
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• W2564282078 doi "https://doi.org/10.1016/j.knosys.2016.12.010" @default.
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