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Abstract
Recommender systems aim to provide item recommendations for users and are usually faced with data sparsity problems (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 of recommender systems with pre-training. The source code of our experiments will be available to facilitate future research.
Highlights
With the rapid development of the Internet, users are faced with information overload, where the large quantity of online items makes it hard for users to make decisions effectively
2) Empirical Results: We present empirical results to show the benefits of pre-training to recommender systems
The rest of the survey is organized as follows: In Sections 2 and 3, we provide a review of existing methods of recommender systems with pre-training; in Section 4, we conduct experiments to empirically show the benefits of pre-training to recommender systems; and in Section 5, we discuss promising directions for future research
Summary
With the rapid development of the Internet, users are faced with information overload, where the large quantity of online items makes it hard for users to make decisions effectively. The benefits of pre-training to recommender systems can be summarized as being twofold: 1) pre-training tasks can better exploit user-item interaction data to capture user interests, and 2) pre-training can help integrate knowledge from different tasks and sources into universal user/item representations, which can be further adapted to various scenarios in recommender systems, such as cold starts and cross-domain transfer. The contributions of this survey can be summarized the following. The rest of the survey is organized as follows: In Sections 2 and 3, we provide a review of existing methods of recommender systems with pre-training; in Section 4, we conduct experiments to empirically show the benefits of pre-training to recommender systems; and in Section 5, we discuss promising directions for future research
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