Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect
It addresses data sparsity issues in recommender systems for users and developers, but is incremental as it synthesizes existing research.
This survey reviews how pre-trained models can transfer knowledge to alleviate data sparsity problems like cold start in recommender systems, and demonstrates benefits through experiments.
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.