DARec: Deep Domain Adaptation for Cross-Domain Recommendation via
Transferring Rating Patterns
Abstract
Cross-domain recommendation has long been
one of the major topics in recommender systems.Recently, various deep models have been proposed to transfer the learned knowledge across domains, but most of them focus on extracting abstract transferable features from auxiliary contents,
e.g., images and review texts, and the patterns in
the rating matrix itself is rarely touched. In this
work, inspired by the concept of domain adaptation, we proposed a deep domain adaptation model
(DARec) that is capable of extracting and transferring patterns from rating matrices only without relying on any auxillary information. We empirically demonstrate on public datasets that our
method achieves the best performance among several state-of-the-art alternative cross-domain recommendation models