Bias and Debias in Recommender System: A Survey and Future Directions

Understand

While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user behavior data.

• However, user behavior data is observational rather than experimental.
• This makes various biases widely exist in the data, including but not limited to selection bias, position bias, exposure bias, and popularity bias.
• Blindly fitting the data without considering the inherent biases will result in many serious issues, e.g., the discrepancy between offline evaluation and online metrics, hurting user satisfaction and trust on the recommendation service, etc.