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arXiv · 2009.09226

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

Zheni Zeng·Chaojun Xiao·Yuan Yao·Ruobing Xie·Zhiyuan Liu·Fen Lin·Leyu Lin·Maosong Sun
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Abstract

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 systems through experiments. Finally, we discuss several promising directions for future research for recommender systems with pre-training.

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Zheni Zeng, Chaojun Xiao, Yuan Yao, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun. 2020-09-19. Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect. https://arxiv.org/abs/2009.09226

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