  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.
