  We assume that recommender systems are more successful, when they are based
on a thorough understanding of how people process information. In the current
paper we test this assumption in the context of social tagging systems.
Cognitive research on how people assign tags has shown that they draw on two
interconnected levels of knowledge in their memory: on a conceptual level of
semantic fields or topics, and on a lexical level that turns patterns on the
semantic level into words. Another strand of tagging research reveals a strong
impact of time dependent forgetting on users' tag choices, such that recently
used tags have a higher probability being reused than "older" tags. In this
paper, we align both strands by implementing a computational theory of human
memory that integrates the two-level conception and the process of forgetting
in form of a tag recommender and test it in three large-scale social tagging
datasets (drawn from BibSonomy, CiteULike and Flickr).
  As expected, our results reveal a selective effect of time: forgetting is
much more pronounced on the lexical level of tags. Second, an extensive
evaluation based on this observation shows that a tag recommender
interconnecting both levels and integrating time dependent forgetting on the
lexical level results in high accuracy predictions and outperforms other
well-established algorithms, such as Collaborative Filtering, Pairwise
Interaction Tensor Factorization, FolkRank and two alternative time dependent
approaches. We conclude that tag recommenders can benefit from going beyond the
manifest level of word co-occurrences, and from including forgetting processes
on the lexical level.
