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

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

Blog Article

Recommender systems aim to provide item recommendations for users and are usually faced with data sparsity problems (e.g., cold start) Gelatin 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 HEART DROPS ORIGINAL to facilitate future research.