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Dual graph attention networks for multi-behavior recommendation

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Abstract

Multi-Behavior Recommendation (MBR) attracts much attention in recent years, whose goal is to improve the prediction of the target behavior (i.e., purchase) by exploiting multi-typed auxiliary behaviors (e.g., view, favorite and add-to-cart). Recently, leveraging Graph Convolutional Networks (GCNs) to capture collaborative signals has been the mainstream paradigm for MBR. However, the existing multi-behavior recommendation methods have suffered from two limitations. On the one hand, personalized user preferences hidden in multi-behavior data are not fully exploited. Users’ multiple types of interactions, such as views, clicks, and so on, offer fine-grained and a deep understanding of the preferences. The importance of different types of behaviors should be carefully distinguished. On the other hand, these methods aggregate the original neighbors of target user and item independently. Users’ preferences may change dynamically with a specific target item. Therefore, users’ dynamic preferences based on specific items should be sufficiently considered. These limitations motivate us to propose a novel recommendation model DGAMR (Dual Graph Attention Networks for Multi-behavior Recommendation), which accurately learns user and item representation by multiple types of behaviors. First, we utilize node-level attention to learn the representation of users and items under specific behavior. Second, behavioral-level attention is used to aggregate different behaviors to generate the final representation of users and items. In addition, we learn the dynamic characteristics of target user and target item by modeling the dependency relation between them. Finally, we utilize the static and dynamic embedding of users/items to predict users’ preferences for items. Extensive experimental results on two real-world datasets clearly show the effectiveness of our proposed model.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://tianchi.aliyun.com/dataset/dataDetail?dataId=649.

  2. https://github.com/chenchongthu/EHCF.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61762078, 62276073, 61966009 and U22A2099), Industrial Support Project of Gansu Colleges (No. 2022CYZC11), Natural Science Foundation of Gansu Province (21JR7RA114), Northwest Normal University Young Teachers Research Capacity Promotion plan (NWNU-LKQN2019-2).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China

    Yunhe Wei, Huifang Ma & Yike Wang

  2. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Huifang Ma & Zhixin Li

  3. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China

    Huifang Ma & Liang Chang

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  1. Yunhe Wei
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Correspondence to Huifang Ma.

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Wei, Y., Ma, H., Wang, Y. et al. Dual graph attention networks for multi-behavior recommendation. Int. J. Mach. Learn. & Cyber. 14, 2831–2846 (2023). https://doi.org/10.1007/s13042-023-01801-0

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