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Graph neural network based model for multi-behavior session-based recommendation

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Abstract

Multi-behavior session-based recommendation aims to predict the next item, such as a location-based service (LBS) or a product, to be interacted by a specific behavior type (e.g., buy or click) in a session involving multiple types of behaviors. State-of-the-art methods generally model multi-behavior dependencies in item-level, but ignore the potential of discovering useful patterns of multi-behavior transition through feature-level representation learning. Besides, sequential and non-sequential patterns should be properly fused in session modeling to capture dynamic interests within the session. To this end, this paper proposes a Graph Neural Network based Hybrid Model GNNH, which enables feature-level deeper representations of multi-behavior interaction sequences for session-based recommendation. Specifically, we first construct multi-relational item graph (MRIG) and feature graph (MRFG) based on session sequences. On top of the MRIG and MRFG, our model takes advantage of GNN to capture item and feature representations, such that global item-to-item and feature-to-feature relations are fully preserved. Afterwards, each multi-behavior session is modeled by a seamless fusion of interacted item and feature representations, where self-attention and mean-pooling are used to obtain sequential and non-sequential patterns simultaneously. Experiments on two real datasets show that the GNNH model significantly outperforms the state-of-the-art methods.

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  1. http://2015.recsyschallenge.com/challege.html

  2. https://tianchi.aliyun.com/competition

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61872258, 61802273, Major project of natural science research in Universities of Jiangsu Province under grant number 20KJA520005.

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Authors and Affiliations

  1. Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Suzhou, China

    Bo Yu, Ruoqian Zhang, Wei Chen & Junhua Fang

  2. Neusoft Corporation, Shenyang, China

    Bo Yu

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  1. Bo Yu
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  2. Ruoqian Zhang
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  3. Wei Chen
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  4. Junhua Fang
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Correspondence to Ruoqian Zhang.

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Yu, B., Zhang, R., Chen, W. et al. Graph neural network based model for multi-behavior session-based recommendation. Geoinformatica 26, 429–447 (2022). https://doi.org/10.1007/s10707-021-00439-w

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