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In this paper, we introduce a tag recommendation algorithm that mimics theway humans draw on items in their long-term memory. This approach uses thefrequency and recency of previous tag assignments to estimate the probabilityof reusing a particular tag. Using three real-world folksonomies gathered frombookmarks in BibSonomy, CiteULike and Flickr, we show how adding atime-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 asimple resource-specific frequency analysis, our algorithm outperforms otherwell-established algorithms, such as FolkRank, Pairwise Interaction TensorFactorization and Collaborative Filtering. We conclude that our approachprovides an accurate and computationally efficient model of a user's temporaltagging behavior. We show how effective principles for information retrievalcan be designed and implemented if human memory processes are taken intoaccount.

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