Domain Adaptation Utilizing Texts and Visions for Cross-domain Recommendations with No Shared Users

In recent years, many researchers focus on cross-domain recommendations (CDRs). CDRs leverage data across multiple services (domains) to enable the transfer of user preferences toward sparse domains. Although some approaches transfer knowledge through shared users across domains, there are privacy concerns. Thus, several CDRs use text features like product reviews, instead of shared users. However, real-world datasets often have noisy text features, complicating effective domain adaptation. In this paper, our objective is to enhance domain adaptation performance while ensuring effective recom-mendation by integrating visual features, alongside text features. First, to mitigate the effect by noisy item descriptions, we incorporate visual features that exclusively capture item-specific information. We obtain effective multimodal representations to improve domain adaptation by employing contrastive learning that aligns visual features with fixed text features. On the other hand, relying solely on this approach could potentially undermine the distinctiveness inherent in both text and images. As a second proposal to address this concern, we minimize mutual information between embeddings of text and image, which encourages the preservation of distinctive features within both modalities. These two proposals lead to a balanced approach to domain adaptation while keeping the effectiveness of recommendation systems. Experimental results show the domain adaptation by the proposed method is effective.