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HGAT-BR: Hyperedge-based graph attention network for basket recommendation

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Abstract

The event that predicts the next item that the people will buy in the next basket is defined as the next basket recommendation task. Though this task has been widely studied, previous works still have the following two challenges: 1) Previous methods usually make predictions via only considering the correlations between items within the basket, while ignoring the similarity relationships between baskets, which may also have the potential capability of improving the basket modeling. 2) Previous studies usually use the Recurrent Neural Network (RNN), especially attention-based RNN and fixed attention mechanisms, to model the relationships between items, which fail to capture the local network structures and their high-order sequential relationships. To overcome the above challenges, in this work, we propose a Hyperedge-based Graph Attention Network for Next Basket Recommendation, namely HGAT-BR, as our solution. To be more specific, to incorporate the similarity relationship between two baskets, we treat baskets as sets of items, and further model them as hyperedges in a hypergraph. Then, the basket representation learning can be converted to the hyperedge embedding task, where a hyperedge-based graph attention network is proposed. To further consider the correlation information among items, we treat items within a basket as nodes in a vanilla graph and learn node representations via another graph neural network. Then, we concatenate these two types of representations to make predictions. Note that, we train the basket and note representation learning simultaneously in an end-to-end manner. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our proposed method compared with several state-of-the-art next basket recommendation methods.

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Notes

  1. In our task, a basket is defined as a collection of items purchased by the same user in a very short period of time.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61602282, No. 11504153) and China Postdoctoral Science Foundation (No. 2016M602181).

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Authors and Affiliations

  1. Shandong Provincial Key Laboratory of Optical Communication Science and Technology, Liaocheng University, Liaocheng, China

    Tengshuo Song, Feng Guo & Zhenbao Feng

  2. Shandong Information Technology Bureau, China Post Group Co., Ltd, Shandong, China

    Haoran Jiang

  3. School of Information Science and Engineering, Shandong Normal University, Shandong, China

    Wenyun Ma

  4. Business School, Shandong Normal University, Shandong, China

    Lei Guo

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  1. Tengshuo Song
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Correspondence to Feng Guo, Zhenbao Feng or Lei Guo.

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Song, T., Guo, F., Jiang, H. et al. HGAT-BR: Hyperedge-based graph attention network for basket recommendation. Appl Intell 53, 1435–1451 (2023). https://doi.org/10.1007/s10489-022-03575-4

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