  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.
