提出新方法挖掘无重叠用户行为，提升跨域序列推荐效果
Cross-Domain Sequential Recommendation via Neural Process
- 用神经过程建模非重叠用户行为，突破依赖重叠用户的传统限制
- 在多个数据集上显著优于现有方法，尤其在长序列场景下提升明显
- 适合研究跨域推荐、用户行为建模的学者与工程师参考
跨域序列推荐（CDSR）旨在通过单一模型预测不同领域的用户下一兴趣项。现有方法主要依赖重叠用户在多领域的行为协同信号，以捕捉跨域项目间关联。然而，重叠用户仅占用户总量的一小部分，这种假设存在偏差，导致非重叠用户的学习不足，制约模型性能。由于缺乏其他领域行为作为协作信号，非重叠用户的行为序列难以用于跨域知识挖掘。为此，本文提出一个挑战性且未被探索的问题：如何释放非重叠用户行为潜力以增强CDSR？为此，我们设计了一种基于神经过程的新框架，有效建模非重叠用户的行为特征，实现更全面的跨域知识迁移。
原文摘要 · Abstract (English)
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?
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