  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. To transform the
large volume of research models into practical improvements, it is highly
urgent to explore the impacts of the biases and perform debiasing when
necessary. When reviewing the papers that consider biases in RS, we find that,
to our surprise, the studies are rather fragmented and lack a systematic
organization. The terminology ``bias'' is widely used in the literature, but
its definition is usually vague and even inconsistent across papers. This
motivates us to provide a systematic survey of existing work on RS biases. In
this paper, we first summarize seven types of biases in recommendation, along
with their definitions and characteristics. We then provide a taxonomy to
position and organize the existing work on recommendation debiasing. Finally,
we identify some open challenges and envision some future directions, with the
hope of inspiring more research work on this important yet less investigated
topic. The summary of debiasing methods reviewed in this survey can be found at
\url{https://github.com/jiawei-chen/RecDebiasing}.
