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arXiv · 1312.5111

Long Time No See: The Probability of Reusing Tags as a Function of Frequency and Recency

Dominik Kowald·Paul Seitlinger·Christoph Trattner·Tobias Ley
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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 adding 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 show how effective principles for information retrieval can be designed and implemented if human memory processes are taken into account.

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Dominik Kowald, Paul Seitlinger, Christoph Trattner, Tobias Ley. 2013-12-18. Long Time No See: The Probability of Reusing Tags as a Function of Frequency and Recency. https://arxiv.org/abs/1312.5111

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