Automatic Hypergraph Generation for Enhancing Recommendation With Sparse Optimization

Abstract

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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2,1}$</tex-math></inline-formula> -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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> and 9.8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> improvement over state-of-the-art baselines regarding Recall and NDCG metrics, respectively.