Modeling time-evolving preferences of users with their sequential item
interactions, has attracted increasing attention in many online applications.
Hence, sequential recommender systems have been developed to learn the dynamic
user interests from the historical interactions for suggesting items. However,
the interaction pattern encoding functions in most existing sequential
recommender systems have focused on single type of user-item interactions. In
many real-life online platforms, user-item interactive behaviors are often
multi-typed (e.g., click, add-to-favorite, purchase) with complex cross-type
behavior inter-dependencies. Learning from informative representations of users
and items based on their multi-typed interaction data, is of great importance
to accurately characterize the time-evolving user preference. In this work, we
tackle the dynamic user-item relation learning with the awareness of
multi-behavior interactive patterns. Towards this end, we propose a new
Temporal Graph Transformer (TGT) recommendation framework to jointly capture
dynamic short-term and long-range user-item interactive patterns, by exploring
the evolving correlations across different types of behaviors. The new TGT
method endows the sequential recommendation architecture to distill dedicated
knowledge for type-specific behavior relational context and the implicit
behavior dependencies. Experiments on the real-world datasets indicate that our
method TGT consistently outperforms various state-of-the-art recommendation
methods. Our model implementation codes are available at
this https URL.

本文提出采用多行为互动模式进行时间变化的用户偏好建模，通过提出的 Temporal Graph Transformer 模型，能够更好地学习不同行为间的相关性，有效解决单一行为矩阵稀疏和数据不足等问题。