DeepAI
Log In Sign Up

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

10/07/2020
by   Jiawei Chen, et al.
0

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.

READ FULL TEXT
01/01/2020

Modeling and Counteracting Exposure Bias in Recommender Systems

What we discover and see online, and consequently our opinions and decis...
08/23/2019

A Survey on Bias and Fairness in Machine Learning

With the widespread use of AI systems and applications in our everyday l...
03/22/2022

A Survey on Techniques for Identifying and Resolving Representation Bias in Data

The grand goal of data-driven decision-making is to help humans make dec...
01/03/2023

Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and Generalization

In the era of information overload, recommender systems (RSs) have becom...
05/20/2020

Adversarial Machine Learning in Recommender Systems: State of the art and Challenges

Latent-factor models (LFM) based on collaborative filtering (CF), such a...
01/29/2020

Correcting for Selection Bias in Learning-to-rank Systems

Click data collected by modern recommendation systems are an important s...
12/20/2022

A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems

Recommendation Systems (RSs) are ubiquitous in modern society and are on...