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- W2040192631 abstract "In this paper, we present work-in-progress of a recently started project that aims at studying the effect of time in recommender systems in the context of social tagging. Despite the existence of previous work in this area, no research has yet made an extensive evaluation and comparison of time-aware recommendation methods. With this motivation, this paper presents results of a study where we focused on understanding (i) when to use the temporal information into traditional collaborative filtering (CF) algorithms, and (ii) how to weight the similarity between users and items by exploring the effect of different time-decay functions. As the results of our extensive evaluation conducted over five social tagging systems (Delicious, BibSonomy, CiteULike, MovieLens, and Last.fm) suggest, the step (when) in which time is incorporated in the CF algorithm has substantial effect on accuracy, and the type of time-decay function (how) plays a role on accuracy and coverage mostly under pre-filtering on user-based CF, while item-based shows stronger stability over the experimental conditions." @default.
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- W2040192631 date "2015-09-16" @default.
- W2040192631 modified "2023-10-16" @default.
- W2040192631 title "Good Times Bad Times" @default.
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- W2040192631 doi "https://doi.org/10.1145/2792838.2799682" @default.
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