2013/12/18 by Dominik Kowald, Paul Seitlinger, Kowald, Dominik +5
Computer Science · Mathematics · #FOS: Computer and information sciences #H.2.8 #H.3.3 #Information Retrieval (cs.IR) #Recommender Systems and Techniques #Tensor decomposition and applications #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1312.5111
openalex publication_date 2013/12/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.