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NAH: neighbor-aware attention-based heterogeneous relation network model in E-commerce recommendation

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Abstract

Traditional recommender systems only utilize a single user-item interaction behavior as the optimization target behavior. However, multi-behavior recommender systems leverage multiple user behaviors as auxiliary behaviors(favorite and page view), which is more practical. Therefore, recommender systems by exploring patterns of multiple behaviors are of great significance in improving performance. Many previous works toward multi-behavior recommendation fail to capture user preference intensity for different items in the heterogeneous graph. Meanwhile, they also ignore high-order relationships that incorporate user different preference intensity into user-item heterogeneous interactions. To solve the above challenges, we propose a novel multi-behavior recommendation model named neighbor-aware attention-based heterogeneous relation network model in E-commerce recommendation (NAH). NAH leverages the attention propagation layer to capture user preference intensity for different items and employs a composition method to incorporate relation embeddings into node embeddings for high-order propagation. Experiment results on two real-world datasets verify the effectiveness of our model in the multi-behavior task by comparing it with some start-of-the-art methods. Further studies verify that our model has a significant effect on exploring high-order information and cold-start users who have few user-item interaction records.

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Availability of supporting data

Beibei dataset and Taobao dataset are public datasets available.

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Acknowledgements

This work is supported by National Natural Science Foundation of China under Grant No. 62172160 and 62062034, Jiangxi Provincial Natural Science Foundation under Grant No. 20212ACB212002, Excellent Scientific and Technological Innovation Teams of Jiangxi Province under Grant No. 20181BCB24009.

Funding

The fundings conclude National Natural Science Foundation of China under Grant No. 62172160 and 62062034, Jiangxi Provincial Natural Science Foundation under Grant No. 20212ACB212002, Excellent Scientific and Technological Innovation Teams of Jiangxi Province under Grant No. 20181BCB24009.

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Authors and Affiliations

  1. College of Information Engineering, East China JiaoTong University, NanChang, 330013, China

    Nan Jiang, Zihao Hu, Jiahui Zhao, Weihao Gu & Ziang Tu

  2. College of Electrical and Automation Engineering, East China JiaoTong University, NanChang, 330013, China

    Jie Wen

  3. College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350108, China

    Ximeng Liu

  4. Azman Hashim International Business School, University of Technology Malaysia, Johor, 81310, Malaysia

    Yuanyuan Li

  5. Jiangxi Academy of Social Sciences, NanChang, 330077, China

    Jianfei Gong

  6. Key Laboratory of Conveyance and Equipment of Ministry of Education, East China JiaoTong University, NanChang, 330013, China

    Fengtao Lin

Authors
  1. Nan Jiang
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  2. Zihao Hu
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  4. Jiahui Zhao
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  8. Yuanyuan Li
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  9. Jianfei Gong
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Contributions

Among the authors in the list, Nan Jiang took charge of researching and revising it critically for intellectual content. Zihao Hu mainly wrote the manuscript and designed the model. Jie Wen and Yuanyuan Li participated in the design of this study. JiahuiZhao and Weihao Gu collected important background information. Ziang Tu and Ximeng Liu designed of the study and the conception. Jianfei Gong and Fengtao Lin revised the manuscript. After consultations, all the authors agreed with the addition of authors in this paper, and all the authors agreed with there arrangement of the names. In the final version of the article, Nan Jiang is tagged as the corresponding author.

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Correspondence to Nan Jiang.

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Jiang, N., Hu, Z., Wen, J. et al. NAH: neighbor-aware attention-based heterogeneous relation network model in E-commerce recommendation. World Wide Web 26, 2373–2394 (2023). https://doi.org/10.1007/s11280-023-01147-1

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