Multi-Domain Sequential Recommendation via Domain Space Learning

This paper explores Multi-Domain Sequential Recommendation (MDSR), an advancement of Multi-Domain Recommendation that incorporates sequential context. Recent MDSR approach exploits domain-specific sequences, decoupled from mixed-domain histories, to model domain-specific sequential preference, and use mixeddomain histories to model domain-shared sequential preference. However, the approach faces challenges in accurately obtaining domain-specific sequential preferences in the target domain, especially when users only occasionally engage with it. In such cases, the history of users in the target domain is limited or not recent, leading the sequential recommender system to capture inaccurate domain-specific sequential preferences. To address this limitation, this paper introduces Multi-Domain Sequential Recommendation via Domain Space Learning (MDSR-DSL). Our approach utilizes cross-domain items to supplement missing sequential context in domain-specific sequences. It involves creating a "domain space" to maintain and utilize the unique characteristics of each domain and a domain-to-domain adaptation mechanism to transform item representations across domain spaces. To validate the effectiveness of MDSR-DSL, this paper extensively compares it with state-of-the-art MD(S)R methods and provides detailed analyses.