Dynamic Graph Collaborative Filtering
This work addresses the problem of providing real-time, accurate recommendations for users in dynamic environments where user interests and item popularity constantly change, offering a substantial improvement over existing methods.
This paper introduces Dynamic Graph Collaborative Filtering (DGCF), a new framework that uses dynamic graphs to capture both collaborative and sequential relationships between users and items. DGCF significantly outperforms state-of-the-art dynamic recommendation methods by up to 30% on three public datasets, especially excelling in datasets with less action repetition.
Dynamic recommendation is essential for modern recommender systems to provide real-time predictions based on sequential data. In real-world scenarios, the popularity of items and interests of users change over time. Based on this assumption, many previous works focus on interaction sequences and learn evolutionary embeddings of users and items. However, we argue that sequence-based models are not able to capture collaborative information among users and items directly. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time. We propose three update mechanisms: zero-order 'inheritance', first-order 'propagation', and second-order 'aggregation', to represent the impact on a user or item when a new interaction occurs. Based on them, we update related user and item embeddings simultaneously when interactions occur in turn, and then use the latest embeddings to make recommendations. Extensive experiments conducted on three public datasets show that DGCF significantly outperforms the state-of-the-art dynamic recommendation methods up to 30. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.