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- W1766918145 abstract "In this paper, we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory. This approach uses the frequency and recency of previous tag assignments to estimate the probability of reusing a particular tag. Using three real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike and Flickr, we show how incorporating a time-dependent component outperforms conventional most popular tags approaches and another existing and very effective but less theory-driven, time-dependent recommendation mechanism. By combining our approach with a simple resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as FolkRank, Pairwise Interaction Tensor Factorization and Collaborative Filtering. We conclude that our approach provides an accurate and computationally efficient model of a user's temporal tagging behavior. We demonstrate how effective principles of information retrieval can be designed and implemented if human memory processes are taken into account." @default.
- W1766918145 created "2016-06-24" @default.
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- W1766918145 date "2014-04-07" @default.
- W1766918145 modified "2023-09-25" @default.
- W1766918145 title "Long time no see" @default.
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- W1766918145 doi "https://doi.org/10.1145/2567948.2576934" @default.
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