Corpus ID: 221819497

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

@article{Zeng2020KnowledgeTV,
  title={Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect},
  author={Zheni Zeng and Chaojun Xiao and Y. Yao and Ruobing Xie and Zhiyuan Liu and Fen Lin and Leyu Lin and M. Sun},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.09226}
}

Zheni Zeng, Chaojun Xiao, +5 authors M. Sun

Published 2020

Computer Science

ArXiv

Recommender systems aim to provide item recommendations for users, and are usually faced with data sparsity problem (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… CONTINUE READING

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed