Frontiers in Big Data (Mar 2021)

Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect

  • Zheni Zeng,
  • Zheni Zeng,
  • Chaojun Xiao,
  • Chaojun Xiao,
  • Yuan Yao,
  • Yuan Yao,
  • Ruobing Xie,
  • Zhiyuan Liu,
  • Zhiyuan Liu,
  • Fen Lin,
  • Leyu Lin,
  • Maosong Sun,
  • Maosong Sun

DOI
https://doi.org/10.3389/fdata.2021.602071
Journal volume & issue
Vol. 4

Abstract

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Recommender systems aim to provide item recommendations for users and are usually faced with data sparsity problems (e.g., cold start) in real-world scenarios. Recently pre-trained models have shown their effectiveness in knowledge transfer between domains and tasks, which can potentially alleviate the data sparsity problem in recommender systems. In this survey, we first provide a review of recommender systems with pre-training. In addition, we show the benefits of pre-training to recommender systems through experiments. Finally, we discuss several promising directions for future research of recommender systems with pre-training. The source code of our experiments will be available to facilitate future research.

Keywords