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SimGNN: simplified graph neural networks for session-based recommendation

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Abstract

Session-based recommender systems (SBR) aim to predict the next action of an anonymous user session. Recently Graph Neural Networks (GNN) models have gained a lot of attention in this task. Existing models learn sequential complex transition patterns using the Gated Graph Neural Networks (GGNN) architecture. We argue that learning non-sequential complex transition patterns may be sufficient in SBR due to the short time interval and length of the sessions. To fully exploit the advantages of non-sequential GNN such as scalability, we design Simplified Graph Neural Network for Session-based Recommendation SimGNN, a non-sequential, linear GNN model for interaction representation. SimGNN uses the k-th power of the normalized adjacency matrix and the current session interactions to learn the k-th layer interaction representation. To improve the representation, SimGNN uses a highway gating mechanism. From the interaction representation learned by the proposed non-sequential and linear model, SimGNN models local preference and global preference and uses a proposed gating mechanism to aggregate these preferences. Experimental results showed that SimGNN outperforms state-of-the-art sequential GGNN models for SBR in terms of accuracy metrics - precision and mean reciprocal ranking.

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Notes

  1. Code will be available at https://github.com/DM-HPC-LAB/SimGNN

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

  3. http://cikm2016.cs.iupui.edu/cikm-cup

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Acknowledgements

This project was partially supported by Grants from Natural Science Foundation of China 62176247. It was also supported by the Fundamental Research Funds for the Central Universities and CAS/TWAS Presidential Fellowship for International Doctoral Students.

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  1. School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, 101400, China

    Tajuddeen Rabiu Gwadabe, Mohammed Ali Mohammed Al-hababi & Ying Liu

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  1. Tajuddeen Rabiu Gwadabe
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Correspondence to Tajuddeen Rabiu Gwadabe.

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Gwadabe, T.R., Al-hababi, M.A.M. & Liu, Y. SimGNN: simplified graph neural networks for session-based recommendation. Appl Intell 53, 22789–22802 (2023). https://doi.org/10.1007/s10489-023-04719-w

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