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DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns - 2019

Darec: Deep Domain Adaptation For Cross-Domain Recommendation Via Transferring Rating Patterns

Research Area:  Machine Learning


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 auxilliary 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 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.


Author(s) Name:  Feng Yuan, Lina Yao, Boualem Benatallah

Journal name:  Machine Learning

Conferrence name:  

Publisher name:  arXiv

DOI:  arXiv:1905.10760

Volume Information: