DCL: Diversified Graph Recommendation With Contrastive Learning

Daohan Su, Bowen Fan, Zhi Zhang, Haoyan Fu, Zhida Qin

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

Diversified recommendation systems have gained increasing popularity in recent years. Nowadays, the emerged graph neural networks (GNNs) have been used to improve the diversity performance. Although some progresses have been made, existing works purely focus on the user–item interactions and overlook the category information, which limits the capability to capture complex diversification among users or items and leads to poor performance. In this article, our target is to integrate full category information into user and item embeddings. To this end, we propose a diversified GNN-based recommendation systems diversified graph recommendation with contrastive learning (DCL). Specifically, we design three key components in our model: 1) the user–item interaction with category-related sampling enhances the interaction of unpopular items; 2) contrastive learning between users and categories shortens the distance of representations between users and their uninteracted categories; and 3) contrastive learning between items and categories diverges the distance of representations between items and their corresponding categories. By applying these three modules, we build a multitask training framework to achieve a balance between accuracy and diversity. Experiments on real-world datasets show that our proposed DCL achieves optimal diversity while paying a little price for accuracy.

源语言英语
页(从-至)1-13
页数13
期刊IEEE Transactions on Computational Social Systems
DOI
出版状态已接受/待刊 - 2024

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Su, D., Fan, B., Zhang, Z., Fu, H., & Qin, Z. (已接受/印刷中). DCL: Diversified Graph Recommendation With Contrastive Learning. IEEE Transactions on Computational Social Systems, 1-13. https://doi.org/10.1109/TCSS.2024.3355780