Article
Version 1
Preserved in Portico This version is not peer-reviewed
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.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
