Bias Issues and Solutions in Recommender System

Recommender systems (RS) have demonstrated great success in information seeking. Recent years have witnessed a large number of work on inventing recommendation 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. 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. To transform the large volume of research models into practical improvements, it is highly urgent to explore the impacts of the biases and develop debiasing strategies when necessary. Therefore, bias issues and solutions in recommender systems have drawn great attention from both academic and industry. In this tutorial, we aim to provide an systemic review of existing work on this topic. We will introduce six types of biases in recommender system, along with their definitions and characteristics; review existing debiasing solutions, along with their strengths and weaknesses; and identify some open challenges and future directions. We hope this tutorial could stimulate more ideas on this topic and facilitate the development of debiasing recommender systems.