DynGCF: Augmenting Inactive Users and Items in Dynamic Graph-based Collaborative Filtering

Modeling user-item interactions in a dynamic manner bring new insight to the representation learning for recommender systems. Distinct from static graph-based approaches that model the whole user-item interaction graph, dynamic graph-based approaches model both the structural and temporal information from a sequence of snapshot graphs. Despite effectiveness, we argue that existing approaches do not explicitly address the temporal sparsity issue, which degrades the representation learning performance for inactive users and items. Therefore, we propose a new Dynamic Graph-based Collaborative Filtering(DynGCF) framework. In particular, it utilizes the vanilla interaction graph with the co-occurrence graph(cograph) to jointly explores 1-hop collaborative and 2-hop implicit similarity for dynamic representation learning. Moreover, to further alleviate temporal sparsity, we explore representative(active) users and items via graph pooling and design an activity-guided gating(AGate) layer to augment inactive users and items. At last, we further stack a temporal aggregator layer to obtain the final representation. We conduct extensive experiments on four real-world benchmark datasets to demonstrate the significant performance gains for DynGCF over several state-of-the-art methods. Further analyses also show the necessity of alleviating temporal sparsity for improving recommendation performance.