  In our work [KPL17], we study temporal usage patterns of Twitter hashtags,
and we use the Base-Level Learning (BLL) equation from the cognitive
architecture ACT-R [An04] to model how a person reuses her own, individual
hashtags as well as hashtags from her social network. The BLL equation accounts
for the time-dependent decay of item exposure in human memory. According to
BLL, the usefulness of a piece of information (e.g., a hashtag) is defined by
how frequently and how recently it was used in the past, following a
time-dependent decay that is best modeled with a power-law distribution. We
used the BLL equation in our previous work to recommend tags in social
bookmarking systems [KL16]. Here [KPL17], we adopt the BLL equation to model
temporal reuse patterns of individual (i.e., reusing own hashtags) and social
hashtags (i.e., reusing hashtags, which has been previously used by a followee)
and to build a cognitive-inspired hashtag recommendation algorithm. We
demonstrate the efficacy of our approach in two empirical social networks
crawled from Twitter, i.e., CompSci and Random (for details about the datasets,
see [KPL17]). Our results show that our approach can outperform current
state-of-the-art hashtag recommendation approaches.
