Sequential Recommendation with Context-Aware Collaborative Graph Attention Networks

Abstract

Recently, sequence features have been extensively studied to improve the performance of recommender systems. However, advanced sequential recommendation methods that rely only on item IDs still face the challenge of modeling fine-grained user preference from interactive data. Furthermore, context-aware sequential recommendations have the hardness of modeling the relationship between items and items, items and users. Both of these two methods ignore the effect of categories on users' next click tendency and the interactive learning between categories and items. In this paper, we propose a method named Contextual Collaborative Graph Attention Network (CCGAT) to model the sequence. Methodologically, user behavior sequences are constructed as graph-structured data, and we apply two similar graph self-attention networks to model the item transitions and the category click probability. CCGAT takes advantage of the fact that users tend to click on the same or similar categories under specific purposes, and provides a simple but effective way to train two networks collaboratively. Extensive experiments on five real-world datasets show that our model outperforms state-of-the-art methods, and demonstrate the validity of modeling both contextual information and graph features.