Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization

被引:2
|
作者
Lin, Zhenghong [1 ]
Yan, Qishan [2 ]
Liu, Weiming [3 ]
Wang, Shiping [1 ]
Wang, Menghan [4 ]
Tan, Yanchao [1 ]
Yang, Carl [5 ]
机构
[1] Fuzhou Univ, Coll Comp & Data Sci, Fuzhou 350116, Peoples R China
[2] Fuzhou Univ, Coll Maynooth Int Engn, Fuzhou 350116, Peoples R China
[3] Zhejiang Univ, Coll Comp Sci, Hangzhou 310027, Peoples R China
[4] eBay Inc, Shanghai 201203, Peoples R China
[5] Emory Univ, Dept Comp Sci, Atlanta, GA 30322 USA
基金
中国国家自然科学基金;
关键词
Recommender systems; hypergraph generation; sparse optimization; graph convolutional network;
D O I
10.1109/TMM.2023.3338083
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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 l( 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.
引用
收藏
页码:5680 / 5693
页数:14
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