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In this paper, we study the imbalance between current state-of-the-art tagrecommendation algorithms and the folksonomy structures of real-world socialtagging systems. While algorithms such as FolkRank are designed for densefolksonomy structures, most social tagging systems exhibit a sparse nature. Toovercome this imbalance, we show that cognitive-inspired algorithms, whichmodel the tag vocabulary of a user in a cognitive-plausible way, can behelpful. Our present approach does this via implementing the activationequation of the cognitive architecture ACT-R, which determines the usefulnessof units in human memory (e.g., tags). In this sense, our long-term researchgoal is to design hybrid recommendation approaches, which combine theadvantages of both worlds in order to adapt to the current setting (i.e.,sparse vs. dense ones).

Overcoming the Imbalance Between Tag Recommendation Approaches and Real-World Folksonomy Structures with Cognitive-Inspired Algorithms