Personalized Graph Sampling for Diverse Short Video Recommendation

Abstract

Efforts to improve diversity in recommendations typically come at a cost of accuracy. While several works have proposed methods to overcome this trade-off, they neglect the unique challenges of short-video platforms: extreme long-tail distributions and hierarchical category structures with multiple granularity levels. Meanwhile, graph neural networks have become the state-of-the-art for recommendation because of their ability to achieve collaborative filtering and learn higher-order relationships between users and items. We therefore aim to unlock the capabilities of GNNs for personalization to adapt diversity to individual user preferences and overcome the accuracy-diversity dilemma in short-video recommendation. In this work, we identify two dimensions of diversity preferences—validated by psychological theory and readily inferable from interaction data and modeled using graph neighbor sampling: user preferences for diverse content across category levels, and preferences for fine-grained diversity within broad-level interests. We integrate these dimensions directly into the graph propagation process through neighbor sampling, enabling the model to dynamically balance exploration of diverse categories with exploitation of user preferences during training. We also employ a curriculum training strategy that schedules exposure to items based on popularity-based hardness to determine how this scheduling improves the latent representations of long-tail items and mitigates the accuracy-diversity trade-off. Through extensive experiments against both standard graph and state-of-the-art graph-based diversified recommendation baselines, we show that our solution achieves superior balance between diversity and accuracy on datasets with extreme long-tail distributions while outperforming state-of-the-art methods at deeper levels of the category hierarchy.