1. The Development of Transferable Recommender Systems
The core goal of recommender systems is to predict the most likely next interaction by modeling the user's historical behavior. This goal is particularly challenging when there is limited user interaction history, which has long plagued the development of recommender systems, known as the cold-start problem. In cold-start scenarios, such as in newly established recommendation platforms with limited interaction sequences for new users, the early stages of model training often suffer from a lack of sufficient sample data. Modeling with limited training data inevitably results in unsatisfactory user recommendations, hindering the growth of the platform. Transfer learning is a solution that both the academic and industrial communities have focused on to address this issue. Introducing pre-trained knowledge into downstream scenarios will greatly alleviate the cold-start problem and help to model user interactions.
Therefore, research on transferable recommender systems has been almost continuous throughout every stage of the development of the recommender systems field. From the era of matrix factorization based on item IDs and user IDs, transferable recommender systems had to achieve transfer learning for ID-based recommender systems based on data overlapping from both source and downstream scenarios. In recent years, there has been rapid development in multimodal understanding technology. Researchers are gradually shifting their focus to modeling user sequences using pure modal information, achieving transferable recommendations even in scenarios where there is no data overlapping between source and downstream scenarios. Currently, ‘one-for-all’ recommender systems that use large language models (LLM) have received a lot of attention. Exploring transferable recommender systems and even foundation models for recommender systems has emerged as the next frontier in the field of recommender systems.