The novel cognitive-inspired tag recommendation approach based on activation processes in human memory provides better recommendation accuracy results than state-of-the-art algorithms. Therfore, we generalized our approaches for related use cases in the area of tag-based recommender systems such as hashtag recommendations in Twitter.
Our first attempt in the research direction of theory-inspired recommender systems has been the development of a tag recommendation approach based on activation processes in human memory. Therefore, in (Kowald, 2015 KC; Kowald & Lex, 2016 KC), we investigated the relationship between activation processes in human memory and the reuse of tags in six social tagging systems. Here, we found that
- past usage frequency,
- the current semantic context
are all important factors when people reuse tags, which corresponds to the theory of activation processes in human memory. Next, in (Kowald & Lex, 2015 KC), we built upon these results in order to utilize the activation equation of the cognitive architecture ACT-R for designing, evaluating and implementing a novel tag recommendation algorithm. Finally, in (Kowald, Pujari & Lex, 2017 KC), Our cognitive-inspired approach termed BLLI,S,C provided better recommendation accuracy results than current state-of-the-art algorithms, which are designed in a more data-driven way.