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.

翻译：跨域推荐在解决数据稀疏性和冷启动问题方面展现了良好的前景。尽管取得了这些进展，现有方法主要依赖域间共享信息（如重叠用户或相同上下文）进行知识迁移，但在缺乏此类条件时难以有效泛化。为应对这些挑战，我们提出利用大多数电子商务系统通用的评论文本。我们的模型（命名为SER）采用三个文本分析模块，并由单一域判别器引导实现解耦表征学习。我们提出了一种新颖的优化策略，既能增强域解耦质量，又能削弱源域中的有害信息。此外，我们将编码网络从单域扩展至多域，实验证明该扩展对基于评论的推荐系统具有显著效果。大量实验和消融研究表明，与现有最先进的单域及跨域推荐方法相比，我们的方法在效率、鲁棒性和可扩展性方面均表现优异。