  Cross-Domain Recommendation (CDR) has been popularly studied to utilize
different domain knowledge to solve the data sparsity and cold-start problem in
recommender systems. In this paper, we focus on the Review-based Non-overlapped
Recommendation (RNCDR) problem. The problem is commonly-existed and challenging
due to two main aspects, i.e, there are only positive user-item ratings on the
target domain and there is no overlapped user across different domains. Most
previous CDR approaches cannot solve the RNCDR problem well, since (1) they
cannot effectively combine review with other information (e.g., ID or ratings)
to obtain expressive user or item embedding, (2) they cannot reduce the domain
discrepancy on users and items. To fill this gap, we propose Collaborative
Filtering with Attribution Alignment model (CFAA), a cross-domain
recommendation framework for the RNCDR problem. CFAA includes two main modules,
i.e., rating prediction module and embedding attribution alignment module. The
former aims to jointly mine review, one-hot ID, and multi-hot historical
ratings to generate expressive user and item embeddings. The later includes
vertical attribution alignment and horizontal attribution alignment, tending to
reduce the discrepancy based on multiple perspectives. Our empirical study on
Douban and Amazon datasets demonstrates that CFAA significantly outperforms the
state-of-the-art models under the RNCDR setting.
