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

• URL: http://arxiv.org/abs/2009.09226v1
• Date: Sat, 19 Sep 2020 13:06:27 GMT
• Title: Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect
• Authors: Zheni Zeng, Chaojun Xiao, Yuan Yao, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin and Maosong Sun
• Abstract summary: We show the benefits of pre-training to recommender systems through experiments. We discuss several promising directions for future research for recommender systems with pre-training.
• Score: 89.91745908462417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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