GraphDPI: Partial label disambiguation by graph representation learning via mutual information maximization

Fan, Jinfu; Yu, Yang; Huang, Linqing; Wang, Zhongjie

doi:10.1016/j.patcog.2022.109133

Cited by 12 publications

(1 citation statement)

References 15 publications

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“…With self-supervised learning, models make full use of relevant information to assist their main task. One branch of self-supervised learning is maximizing mutual information [18][19][20], which has achieved significant progress in computer vision [21], audio processing [22,23], natural language understanding [24], and so on. There are existing works that integrate self-supervised learning with recommendation systems.…”

Section: Self-supervised Learning For Recommendationmentioning

confidence: 99%

Popularity-Debiased Graph Self-Supervised for Recommendation

Li,

Hu,

Guo

et al. 2024

Electronics

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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 for a large proportion) be neglected. How to mitigate the bias caused by item popularity has become one of the hot topics in current research. To address the above problems, we propose a Popularity-Debiased Graph Self-Supervised for Recommendation (PDGS). Specifically, we apply a penalty constraint on item popularity during the data enhancement process on the user–item interaction graph to eliminate the inherent popularity bias. We generate item similarity graphs with the popularity bias removed to construct a self-supervised learning task under multiple views, and we design model optimization strategies from the perspectives of popular items and long-tail items to generate recommendation lists. We conduct a large number of comparison experiments, as well as ablation experiments, on three public datasets to verify the effectiveness and the superiority of the model in balancing recommendation accuracy and diversity.

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