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A Cross-Domain Recommender System for Literary Books Using Multi-Head Self-Attention Interaction and Knowledge Transfer Learning

Author

Listed:
  • Yuan Cui

    (Commerce Department, Shanxi Professional College of Finance, China & Lincoln University College, Malaysia)

  • Yuexing Duan

    (College of Information and Computer, Taiyuan University of Technology, China)

  • Yueqin Zhang

    (College of Information and Computer, Taiyuan University of Technology, China)

  • Li Pan

    (Zhengzhou Institute of Engineering and Technology, China & UCSI University, Malaysia)

Abstract

Existing book recommendation methods often overlook the rich information contained in the comment text, which can limit their effectiveness. Therefore, a cross-domain recommender system for literary books that leverages multi-head self-attention interaction and knowledge transfer learning is proposed. Firstly, the BERT model is employed to obtain word vectors, and CNN is used to extract user and project features. Then, higher-level features are captured through the fusion of multi-head self-attention and addition pooling. Finally, knowledge transfer learning is introduced to conduct joint modeling between different domains by simultaneously extracting domain-specific features and shared features between domains. On the Amazon dataset, the proposed model achieved MAE and MSE of 0.801 and 1.058 in the “movie-book” recommendation task and 0.787 and 0.805 in the “music-book” recommendation task, respectively. This performance is significantly superior to other advanced recommendation models. Moreover, the proposed model also has good universality on the Chinese dataset.

Suggested Citation

  • Yuan Cui & Yuexing Duan & Yueqin Zhang & Li Pan, 2023. "A Cross-Domain Recommender System for Literary Books Using Multi-Head Self-Attention Interaction and Knowledge Transfer Learning," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 19(1), pages 1-22, January.
    • Handle: RePEc:igg:jdwm00:v:19:y:2023:i:1:p:1-22
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    1. Mohiuddin Ahmed, 2018. "Collective Anomaly Detection Techniques for Network Traffic Analysis," Annals of Data Science, Springer, vol. 5(4), pages 497-512, December.
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