Multi-Behavior Sequential Recommendation with Temporal Graph Transformer

TL;DR

This paper introduces a Temporal Graph Transformer framework that models multi-typed, time-evolving user-item interactions to improve sequential recommendation accuracy by capturing complex behavior dependencies.

Contribution

It proposes a novel TGT model that jointly learns short-term and long-term user-item interaction patterns across multiple behavior types.

Findings

TGT outperforms state-of-the-art methods on real-world datasets.

The model effectively captures cross-type behavior dependencies.

Experimental results demonstrate improved recommendation accuracy.

Abstract

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 https://github.com/akaxlh/TGT.