Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation

Yoonhyuk Choi

Abstract:

A cross-domain recommendation has shown promising results in
solving data-sparsity and cold-start problems. Despite such progress,
existing methods focus on domain-shareable information (overlapped users or same contexts) for a knowledge transfer, and they
fail to generalize well without such requirements. To deal with
these problems, we suggest utilizing review texts that are general to most e-commerce systems. Our model (named SER) uses
three text analysis modules, guided by a single domain discriminator for disentangled representation learning. Here, we suggest a
novel optimization strategy that can enhance the quality of domain
disentanglement, and also debilitates detrimental information of
a source domain. Also, we extend the encoding network from a
single to multiple domains, which has proven to be powerful for
review-based recommender systems. Extensive experiments and ablation studies demonstrate that our method is efficient, robust, and
scalable compared to the state-of-the-art single and cross-domain
recommendation methods.