Explicitly Modeling Relationships between Domain-Specific and Domain-Invariant Interests for Cross-Domain Recommendation

This paper focuses on cross-domain recommendation (CDR) without auxiliary information. Existing works on CDR all ignore the bi-directional transformation relationships between users' domain-invariant interests and domain-specific interests. Moreover, they only rely on the sparse interactions as supervised signals for model training, which can not guarantee the generated representations are effective. In response to the limitation of these existing works, we propose a model named MRCDR which explicitly models relationships between domain-specific and domain-invariant interests for cross-domain recommendation. We project the domain-specific representations of users to a common space generating their domain-invariant representations. To remedy the problem of insufficient supervised signals, we propose two strategies that generate extra self-supervision signals to enhance model training. The aligned strategy tries to make the two domain-invariant representations an overlapped user to be consistent. The cycle strategy tries to make the reversely projected representation of the domain-invariant representation of a non-overlapped user to be consistent with its original domain-specific representation. We conduct extensive experiments on real-world datasets and the results show the effectiveness of our proposed model against the state-of-the-art methods.