Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization

超图 计算机科学 推荐系统 聚类分析 图形 理论计算机科学 情报检索 机器学习 数学 离散数学
作者
Zhenghong Lin,Qishan Yan,Weiming Liu,Shiping Wang,Menghan Wang,Yanchao Tan,Carl Yang
出处
期刊:IEEE Transactions on Multimedia [Institute of Electrical and Electronics Engineers]
卷期号:26: 5680-5693 被引量:1
标识
DOI:10.1109/tmm.2023.3338083
摘要

With the rapid growth of activities on the web, large amounts of interaction data on multimedia platforms are easily accessible, including e-commerce, music sharing, and social media. By discovering various interests of users, recommender systems can improve user satisfaction without accessing overwhelming personal information. Compared to graph-based models, hypergraph-based collaborative filtering has the ability to model higher-order relations besides pair-wise relations among users and items, where the hypergraph structures are mainly obtained from specialized data or external knowledge. However, the above well-constructed hypergraph structures are often not readily available in every situation. To this end, we first propose a novel framework named HGRec, which can enhance recommendation via automatic hypergraph generation. By exploiting the clustering mechanism based on the user/item similarity, we group users and items without additional knowledge for hypergraph structure learning and design a cross-view recommendation module to alleviate the combinatorial gaps between the representations of the local ordinary graph and the global hypergraph. Furthermore, we devise a sparse optimization strategy to ensure the effectiveness of hypergraph structures, where a novel integration of the $\ell _{2,1}$ -norm and optimal transport framework is designed for hypergraph generation. We term the model HGRec with sparse optimization strategy as HGRec++. Extensive experiments on public multi-domain datasets demonstrate the superiority brought by our HGRec++, which gains average 8.1 $\%$ and 9.8 $\%$ improvement over state-of-the-art baselines regarding Recall and NDCG metrics, respectively.
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