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

• URL: http://arxiv.org/abs/2110.12648v3
• Date: Wed, 12 Apr 2023 08:56:06 GMT
• Title: Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation
• Authors: Yoonhyuk Choi, Jiho Choi, Taewook Ko, Hyungho Byun, Chong-Kwon Kim
• Abstract summary: Cross-domain recommendation has shown promising results in solving data-sparsity and cold-start problems. Our model (named SER) uses three text analysis modules, guided by a single domain discriminator for disentangled representation learning.
• Score: 1.2074552857379273
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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