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Knowledge distillation for multi-depth-model-fusion recommendation algorithm

Author

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  • Mingbao Yang
  • Shaobo Li
  • Peng Zhou
  • JianJun Hu

Abstract

Recommendation algorithms save a lot of valuable time for people to get the information they are interested in. However, the feature calculation and extraction process of each machine learning or deep learning recommendation algorithm are different, so how to obtain various features with different dimensions, i.e., how to integrate the advantages of each model and improve the model inference efficiency, becomes the focus of this paper. In this paper, a better deep learning model is obtained by integrating several cutting-edge deep learning models. Meanwhile, to make the integrated learning model converge better and faster, the parameters of the integrated module are initialized, constraints are imposed, and a new activation function is designed for better integration of the sub-models. Finally, the integrated large model is distilled for knowledge distillation, which greatly reduces the number of model parameters and improves the model inference efficiency.

Suggested Citation

  • Mingbao Yang & Shaobo Li & Peng Zhou & JianJun Hu, 2022. "Knowledge distillation for multi-depth-model-fusion recommendation algorithm," PLOS ONE, Public Library of Science, vol. 17(10), pages 1-19, October.
    • Handle: RePEc:plo:pone00:0275955
    DOI: 10.1371/journal.pone.0275955
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    References listed on IDEAS

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    1. Jungpyo Lee & So Young Sohn, 2021. "Recommendation system for technology convergence opportunities based on self-supervised representation learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 1-25, January.
    2. Zhaoyi Li & Fei Xiong & Ximeng Wang & Hongshu Chen & Xi Xiong, 2019. "Topological Influence-Aware Recommendation on Social Networks," Complexity, Hindawi, vol. 2019, pages 1-12, February.
    3. Tam The Nguyen & Tung Thanh Nguyen, 2021. "PERSONA: A personalized model for code recommendation," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-27, November.
    4. Nurul Aida Osman & Shahrul Azman Mohd Noah & Mohammad Darwich & Masnizah Mohd, 2021. "Integrating contextual sentiment analysis in collaborative recommender systems," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-21, March.
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