Causal Recommendation: Progresses and Future Directions

Data-driven recommender systems have demonstrated great success in various Web applications owing to the extraordinary ability of machine learning models to recognize patterns (ie correlation) from users' behaviors. However, they still suffer from several issues such as biases and unfairness due to spurious correlations. Considering the causal mechanism behind data can avoid the influences of such spurious correlations. In this light, embracing causal recommender modeling is an exciting and promising direction.