CDSRNP: Cross-Domain Sequential Recommendation via Neural Process
Haipeng Li, Jiangxia Cao, Yiwen Gao, Yunhuai Liu, Shuchao Pang · Society for Industrial and Applied Mathematics eBooks · 2025
Cross-Domain Sequential Recommendation (CDSR) is a hot topic in sequence-based user interest modeling, which aims at utilizing a single model to predict the next items for different domains. To tackle the CDSR, many methods are focused on domain overlapped users’ behaviors fitting, which heavily relies on the same user’s different-domain item sequences collaborating signals to capture the synergy of cross-domain item-item correlation. Indeed, these overlapped users occupy a small fraction of the entire user set only, which introduces a strong assumption that the small group of domain overlapped users is enough to represent all domain user behavior characteristics. However, intuitively, such a suggestion is biased, and the insufficient learning paradigm in non-overlapped users will inevitably limit model performance. Further, it is not trivial to model non-overlapped user behaviors in CDSR because there are no other domain behaviors to collaborate with, which causes the observed single-domain users’ behavior sequences to be hard to contribute to cross-domain knowledge mining. Considering such a phenomenon, we raise a challenging and unexplored question: How to unleash the potential of non-overlapped users’ behaviors to empower CDSR? To this end, we propose a novel CDSR framework with Neural Processes (NP), briefly termed CDSRNP, where NP combines the advantages of meta-learning and stochastic processes. As a meta-learning based method, we first sample some observed overlapped users’ behaviors as the support set to empower query users’ prediction. Next, we employ the NP principle to align the cross-domain correlation prior/posterior distributions generated by support/query user sets, thus the query user (e.g., non-overlapped user) behaviors sequence could also establish a straight bridge to connect other domain items. Additionally, we design a fine-grained interest adaptive layer to identify the users’ interests to enhance prediction. Experimental results illustrate that CDSRNP1 outperforms state-of-the-art methods in two real-world datasets.