  So far it is unclear how different factors of a scientific publication
recommender system based on users' tweets have an influence on the
recommendation performance. We examine three different factors, namely
profiling method, temporal decay, and richness of content. Regarding profiling,
we compare CF-IDF that replaces terms in TF-IDF by semantic concepts, HCF-IDF
as novel hierarchical variant of CF-IDF, and topic modeling. As temporal decay
functions, we apply sliding window and exponential decay. In terms of the
richness of content, we compare recommendations using both full-texts and
titles of publications and using only titles. Overall, the three factors make
twelve recommendation strategies. We have conducted an online experiment with
123 participants and compared the strategies in a within-group design. The best
recommendations are achieved by the strategy combining CF-IDF, sliding window,
and with full-texts. However, the strategies using the novel HCF-IDF profiling
method achieve similar results with just using the titles of the publications.
Therefore, HCF-IDF can make recommendations when only short and sparse data is
available.
