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• W2891788960 abstract "Similarity computation is the critical component of collaborative filtering recommendation. Traditional similarity measures are usually calculated on user-item rating matrix directly, and couldn’t well describe users’ internal similar relations and personalization of rating habits. This paper proposes a novel similarity framework based on latent factor model, named LFSF (Latent Factor-based Similarity Framework). Different from the traditional similarity calculation based on rating matrix, the LFSF uses latent factor vectors instead of rating vectors to compute similarity. Since the latent factors are learned from the user feedback by the LFMs, the proposed LFSF appropriately merges advantages of the LFM into the similarity calculation, which improves the predictive accuracy of similarity measures. We illustrate the proposed framework in detail and propose related application instance of LFSF in this paper. We find that latent factors indicate the user preferences and reflect the item attributes more specifically, and the value of latent factors are less affected by user rating habits and item popularity. Experiments based on the real datasets show that the proposed LFSF is more rational and effective than traditional rating-based similarity." @default.
• W2891788960 created "2018-09-27" @default.
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• W2891788960 date "2018-01-01" @default.
• W2891788960 modified "2023-09-27" @default.
• W2891788960 title "LFSF: Latent Factor-Based Similarity Framework and Its Application for Collaborative Recommendation" @default.
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• W2891788960 doi "https://doi.org/10.1007/978-3-030-00776-8_68" @default.
• W2891788960 hasPublicationYear "2018" @default.
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