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

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

JOURNAL=Frontiers in Big Data

VOLUME=4

YEAR=2021

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2021.602071

DOI=10.3389/fdata.2021.602071

ISSN=2624-909X

ABSTRACT=<p>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.</p>