Datensatz

Zusammenfassung

Recommender systems are most successful for popular items and users with
ample interactions (likes, ratings etc.). This work addresses the difficult and
underexplored case of supporting users who have very sparse interactions but
post informative review texts. Our experimental studies address two book
communities with these characteristics. We design a framework with
Transformer-based representation learning, covering user-item interactions,
item content, and user-provided reviews. To overcome interaction sparseness, we
devise techniques for selecting the most informative cues to construct concise
user profiles. Comprehensive experiments, with datasets from Amazon and
Goodreads, show that judicious selection of text snippets achieves the best
performance, even in comparison to ChatGPT-generated user profiles.