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Bundle recommendation model based on complete hypergraph neural network

Wang Haonan1
He Ping'an2
Dai Qi3
1. School of computer science & technology(School of artificial intelligence), Zhejiang Sci-Tech University, Hangzhou 310018, China
2. School of science, Zhejiang Sci-Tech University, Hangzhou 310018, China
3. School of life sciences & medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China

Abstract

Bundling recommendation enhances user experience and boosts merchant sales performance by offering predefined sets of product combinations. It also plays a significant role in various service ecosystems such as video-on-demand and music playlist generation. Existing bundling recommendation methods often rely on shared model parameters or multi-task learning schemes, neglecting the deep-level connections among users, items, and bundles, which leads to information loss and impacts the performance of recommendation systems. To address these issues, this paper proposes a complete hypergraph neural network (CHNN) . First, the framework constructs a complete hypergraph to express the ternary relationships among users, items, and bundles. These ternary relationships not only include the interconnections among users, items, and bundles but also encompass the internal connections within users and bundles, effectively describing the relationship between product bundles and user preferences. Second, the model consists of an initialization layer, a triple convolution layer, and a prediction layer. The initialization layer generates embedding vectors for each user, item, and bundle. The triple convolution layer extracts information from the complete hypergraph and leverages the user-bundle graph and item-bundle graph to enhance the representations of users, items, and bundles. The prediction layer provides recommendations based on the final embedding vectors. Through multi-layer rich convolution operations, the model fully explores the associations contained in the complete hypergraph to achieve more accurate recommendations. Experiments on two real-world datasets, NetEase and YouShu, demonstrate that CHNN achieves an average improvement of 2.4% in Recall and 2.75% in NDCG, outperforming existing baseline models and showcasing its effectiveness in the field of bundling recommendation.

Foundation Support

国家自然科学基金资助项目(62172369)
浙江省高层次人才特殊支持计划(2021R52019)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.11.0494
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 7

Publish History

[2025-03-14] Accepted Paper

Cite This Article

王浩南, 贺平安, 代琦. 基于完整超图神经网络的捆绑推荐模型 [J]. 计算机应用研究, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0494. (Wang Haonan, He Ping'an, Dai Qi. Bundle recommendation model based on complete hypergraph neural network [J]. Application Research of Computers, 2025, 42 (7). (2025-03-14). https://doi.org/10.19734/j.issn.1001-3695.2024.11.0494. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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