Popularity-Debiased Graph Self-Supervised for Recommendation
Shanshan Li,
Xinzhuan Hu,
Jingfeng Guo
et al.
Abstract:The rise of graph neural networks has greatly contributed to the development of recommendation systems, and self-supervised learning has emerged as one of the most important approaches to address sparse interaction data. However, existing methods mostly focus on the recommendation’s accuracy while neglecting the role of recommended item diversity in enhancing user interest and merchant benefits. The reason for this phenomenon is mainly due to the bias of popular items, which makes the long-tail items (account … Show more
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