This is published under the terms of CC BY-NC-ND 2.0.
In recent years, several successful tag recommendation mechanisms have been developed that, among others, built upon Collaborative Filtering, Tensor Factorization, graph-based algorithms and simple “most popular tags” approaches. From an economic perspective, the latter approach has been convincing as calculating frequencies is computationally efficient and has shown to be effective with respect to different recommender evaluation metrics. In order to extend these conventional “most popular tags” approaches we introduce a tag recommendation algorithm that mimics the way humans draw on items in their long-term memory. Based on a theory of human memory, the approach estimates a tag’s reuse probability as a function of usage frequency and recency in the user’s past (base-level activation) as well as of the current semantic context (associative component). Using four real-world folksonomies gathered from bookmarks in BibSonomy, CiteULike, Delicious and Flickr, we show how refining frequency-based estimates, by considering recency and semantic context, 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 resource-specific frequency analysis, our algorithm outperforms other well-established algorithms, such as Collaborative Filtering, FolkRank and Pairwise Interaction Tensor Factorization with respect to recommender accuracy and runtime. We conclude that our approach provides an accurate and computationally efficient model of a user’s temporal tagging behavior. Moreover, we demonstrate how effective principles of recommender systems can be designed and implemented if human memory processes are taken into account.