  Cross-domain recommendation (CDR) aims to provide better recommendation
results in the target domain with the help of the source domain, which is
widely used and explored in real-world systems. However, CDR in the matching
(i.e., candidate generation) module struggles with the data sparsity and
popularity bias issues in both representation learning and knowledge transfer.
In this work, we propose a novel Contrastive Cross-Domain Recommendation (CCDR)
framework for CDR in matching. Specifically, we build a huge diversified
preference network to capture multiple information reflecting user diverse
interests, and design an intra-domain contrastive learning (intra-CL) and three
inter-domain contrastive learning (inter-CL) tasks for better representation
learning and knowledge transfer. The intra-CL enables more effective and
balanced training inside the target domain via a graph augmentation, while the
inter-CL builds different types of cross-domain interactions from user,
taxonomy, and neighbor aspects. In experiments, CCDR achieves significant
improvements on both offline and online evaluations in a real-world system.
Currently, we have deployed our CCDR on WeChat Top Stories, affecting plenty of
users. The source code is in https://github.com/lqfarmer/CCDR.
