Meta-Learning for Recommendation System with the Multi-Tasking Learning Setting

Abstract

In the recommendation system, the number of users is often very large, but each user's interaction behavior is very limited. For a specific user, how to fully utilize the less interactive data to train a personalized recommendation model is a very challenging problem. In this paper, we introduce the framework of multi-tasking learning into the recommendation system and regard the recommendation of different users as different tasks with certain commonalities. To solve this multi-tasking learning problem, we apply the task-agnostic meta-learning(TAML) in the model. The experimental results on the public datasets MovieLens illustrate those recommendation models trained in the multi-tasking learning setting by the meta-learning in this paper outperform the most deep recommendation models and get the best performance.