Contrastive representation learning in recommendation systems--The investigation of the performance of the self-supervised learning in large-scale recommendation systems

Research Article
Open access

Contrastive representation learning in recommendation systems--The investigation of the performance of the self-supervised learning in large-scale recommendation systems

Yuxin Li 1* , Jingyi Wang 2 , Xinyang Wu 3 , Rui Zhou 4 , Baichuan Xu 5
  • 1 University of Illinois at Urbana-Champaign    
  • 2 Hunan University    
  • 3 East China University of Science and Technology    
  • 4 University of Illinois at Urbana-Champaign    
  • 5 Communication University of China    
  • *corresponding author liyuxin_icc_7@163.com
Published on 8 December 2023 | https://doi.org/10.54254/2753-8818/19/20230568
TNS Vol.19
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-83558-203-9
ISBN (Online): 978-1-83558-204-6

Abstract

Self-supervised learning (SSL) has been proposed in machine learning projects for its convenience of reducing self-labeled datasets in recent years. However, the implementation of SSL in large-scale recommendation systems has lagged behind the evolution because of their scarce and tailed characteristics. In 2021, an article proposed the use of SSL in recommendation systems to pursue an improvement in the performance of recommender models. This article is built on this previous investigation and aims to further explore the role of SSL in recommendation systems and to investigate an improvement of the model’s efficiency. To answer research questions, this paper tests three models with different numbers of towers to discover the best performance of the use of SSL in recommender models. Consequently, it is found that implementing SSL on the item side only (two-tower DNNs) produced the best result. Then, when constructing the two-tower DNNs model, this article examines different numbers of negative pairs to change the InfoNCE loss to investigate a tradeoff between the number of positive and negative samples in the performance of the model. As a result, it turns out to be a weak correlation between this ratio and the performance; Hence, it is concluded that the change of the number of positive and negative samples would not necessarily affect the two-tower DNNs model. In our experiential stage, this paper uses a real-world dataset with 100k training samples to testify and compare our results.

Keywords:

contrastive representation learning, super-supervised learning, recommendation systems, neural networks, two-tower DNN.

Li,Y.;Wang,J.;Wu,X.;Zhou,R.;Xu,B. (2023). Contrastive representation learning in recommendation systems--The investigation of the performance of the self-supervised learning in large-scale recommendation systems. Theoretical and Natural Science,19,257-264.
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References

[1]. Zhou, K., Wang, H., Zhao, W. X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., & Wen, J.-R. (2020). S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. https://doi.org/10.48550/arXiv:2008.07873

[2]. Yao, T., Yi, X., Cheng, D. Z., Xu, F., Chen, T., Menon, A., Hong, L., Chi, E. H., Tjoa, S., Kang, J. (Jay), & Ettinger, E. (2021). Self-supervised Learning for Large-scale Item Recommendations. https://doi.org/10.48550/arXiv.2007.12865

[3]. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. https://doi.org/10.48550/arXiv:2002.05709v3

[4]. Yu, J., Yin, H., Xia, X., Chen, T., Li, J., & Huang, Z. (2022). Self-Supervised Learning for Recommender Systems: A Survey. https://doi.org/10.48550/arXiv.2203.15876

[5]. He, X., Chen, T., Kan, M., Chen, X. (2015). TriRank: Review-aware Explainable Recommendation by Modeling Aspects. http://doi.org/10.1145/2806416.2806504

Cite this article

Li,Y.;Wang,J.;Wu,X.;Zhou,R.;Xu,B. (2023). Contrastive representation learning in recommendation systems--The investigation of the performance of the self-supervised learning in large-scale recommendation systems. Theoretical and Natural Science,19,257-264.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics

ISBN：978-1-83558-203-9(Print) / 978-1-83558-204-6(Online)
Editor：Marwan Omar, Roman Bauer
Conference website: https://www.confciap.org/
Conference date: 25 March 2023
Series: Theoretical and Natural Science
Volume number: Vol.19
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. Zhou, K., Wang, H., Zhao, W. X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., & Wen, J.-R. (2020). S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization. https://doi.org/10.48550/arXiv:2008.07873

[2]. Yao, T., Yi, X., Cheng, D. Z., Xu, F., Chen, T., Menon, A., Hong, L., Chi, E. H., Tjoa, S., Kang, J. (Jay), & Ettinger, E. (2021). Self-supervised Learning for Large-scale Item Recommendations. https://doi.org/10.48550/arXiv.2007.12865

[3]. Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. https://doi.org/10.48550/arXiv:2002.05709v3

[4]. Yu, J., Yin, H., Xia, X., Chen, T., Li, J., & Huang, Z. (2022). Self-Supervised Learning for Recommender Systems: A Survey. https://doi.org/10.48550/arXiv.2203.15876

[5]. He, X., Chen, T., Kan, M., Chen, X. (2015). TriRank: Review-aware Explainable Recommendation by Modeling Aspects. http://doi.org/10.1145/2806416.2806504

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