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Item trend learning for sequential recommendation system using gated graph neural network

  • S.I. : Deep Social Computing
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Abstract

Recommendation system, or recommender system, is widely used in online Web applications like e-commerce Web sites and movie review Web sites. Sequential recommender put more emphasis upon user’s short-term preference through exploiting information from its recent history. By incorporating the user short-term preference into the recommendation, the algorithm achieves a higher accuracy, which proves that a more accurate user portrait or representation boosts the performance to a great extent. Intuitionally, we seek to improve the current item representation modeling via incorporating the item trend information. Most of the recommendation models neglect the importance of the ever-changing item popularity. To this end, this paper introduces a novel sequential recommendation approach dubbed TRec. TRec learns the item trend information from the implicit user interaction history and incorporates the item trend information into the subsequent item recommendation tasks. After that, a self-attention mechanism is used for better representation. We also investigate alternative ways to model the proposed item trend representation; we evaluate two variant models which leverage the power of gated graph neural network upon the item trend representation modeling to boost the representation ability. We conduct extensive experiments with seven baseline methods on four benchmark datasets. The empirical results show that our proposed approach outperforms the state-of-the-art models as high as 18.2%. The experiment result displays the effectiveness in item trend information learning while with low computational complexity as well. Our study demonstrates the importance of item trend information in recommendation system.

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Notes

  1. https://grouplens.org/datasets/MovieLens/.

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Acknowledgements

This work is supported in part by Griffith Industry Collaborative Grant. This work is supported in part by the National Key Research and Development Program of China under Grant 2017YFE0117500. This work is supported in part by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (Grant No. 17KJB520028), Tongda College of Nanjing University of Posts and Telecommunications (Grant No. XK203XZ18002).

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Authors and Affiliations

  1. School of ICT, Griffith University, Gold Coast, Australia

    Ye Tao & Can Wang

  2. School of Computer Science, University of New South Wales, Sydney, Australia

    Lina Yao

  3. School of Computer Engineering and Science, Shanghai University, Shanghai, China

    Weimin Li

  4. Tongda College, Nanjing University of Posts and Telecommunications, Nanjing, China

    Yonghong Yu

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  1. Ye Tao
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  2. Can Wang
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Correspondence to Can Wang.

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Tao, Y., Wang, C., Yao, L. et al. Item trend learning for sequential recommendation system using gated graph neural network. Neural Comput & Applic 35, 13077–13092 (2023). https://doi.org/10.1007/s00521-021-05723-2

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