Article
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A Global Structural Hypergraph Convolution Model for Bundle Recommendation
Version 1
: Received: 4 August 2023 / Approved: 7 August 2023 / Online: 8 August 2023 (05:04:44 CEST)
A peer-reviewed article of this Preprint also exists.
Liu, X.; Yuan, M. A Global Structural Hypergraph Convolutional Model for Bundle Recommendation. Electronics 2023, 12, 3952. Liu, X.; Yuan, M. A Global Structural Hypergraph Convolutional Model for Bundle Recommendation. Electronics 2023, 12, 3952.
Abstract
Bundle recommendation is provided to users by combining related items into a bundle, enhancing the user's shopping experience and increase the merchant's sales revenue. Despite the success of existing solutions based on Graph Neural Networks(GNN), there are still some significant challenges: (1)It is demanding to explicitly model multiple complex associations using standard graph neural networks. (2)Numerous additional nodes and edges are introduced to approximate higher-order associations. (3)The user-bundle historical interaction data is highly sparse. In this work, we propose a Global Structural Hypergraph Convolution model for Bundle Recommendation (SHCBR) to address the above problems. Specifically, we jointly incorporate multiple complex interactions between users, items, and bundles(three entities) into a relational hypergraph without introducing additional nodes and edges. Higher-order associations are already involved in the hypergraph structure which alleviate the training burden of neural networks and the dilemma of scarce data. In addition, we design a special matrix propagation rule that captures non-pairwise complex relationships between entities. Using item nodes as links, hypergraph convolution propagation between user and bundle nodes generates the learned representations. Experimental results on two real-world datasets demonstrate that SHCBR outperforms the state-of-the-art baselines by 11.07%-25.66\% on Recall and 16.81%-33.53% on NDCG. We publicly release the codes and datasets at https://github.com/Lxtdzh/SHCBR-master/.
Keywords
recommender system; bundle recommendation; hypergraph neural network; hypergraph
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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