Improving Long-Tail Item Recommendation with Graph Augmentation

Sichun Luo, Chen Ma, Yuanzhang Xiao, Linqi Song

Abstract: The ubiquitous long-tail distribution of inherent user behaviors results in worse recommendation performance for the items with fewer user records (i.e., tail items) than those with richer ones (i.e., head items). Graph-based recommendation methods (e.g., using graph neural networks) have recently emerged as a powerful tool for recommender systems, often outperforming traditional methods. However, existing techniques for alleviating the long-tail problem mainly focus on traditional methods. There is a lack of graph-based methods that can efficiently deal with the long-tail problem. In this paper, we propose a novel approach, Graph Augmentation for Long-tail Recommendation (GALORE), which can be plugged into any graph-based recommendation models to improve the performance for tail items. GALORE incorporates an edge addition module that enriches the graph's connectivity for tail items by injecting additional item-to-item edges. To further balance the graph structure, GALORE utilizes a degree-aware edge dropping strategy, preserving the more valuable edges from the tail items while selectively discarding less informative edges from the head items. Beyond structural augmentation, we synthesize new data samples, thereby addressing the data scarcity issue for tail items. We further introduce a two-stage training strategy to facilitate the learning for both head and tail items. Comprehensive empirical studies conducted on four datasets show that GALORE outperforms existing methods in terms of the performance for tail items as well as the overall performance.