Multi-task learning for collaborative filtering

In the recommender system, the user's historical behavior data is one of the most important sources of the system's input data. According to the user's feedback mechanism, behavior data can be divided into explicit feedback data and implicit feedback data. However, most recommendation algorithms focus separately on explicit feedback or implicit feedback. How to combine explicit and implicit feedback for recommendation tasks has always been a research problem. In recent years, deep learning technology has dominated the research on recommendation algorithms. But even the latest neural network-based recommendation algorithm cannot exceed classic methods (such as matrix factorization) in most cases. In this work, we propose a new collaborative filtering framework with neural network architecture. On the one hand, we use both explicit feedback data and implicit feedback data as input to learn multiple representations of users and items. On the other hand, we use multi-task learning to optimize our framework and use two relatively simple auxiliary tasks to enhance the generalization ability of our framework. Extensive experiments on five real-world datasets show significant improvements in our proposed framework over the state-of-the-art methods and vanilla matrix factorization.