# Mitigating Position Bias with Regularization for Recommender Systems

**Authors:** Hao Wang  
**arXiv:** [2401.16427](https://arxiv.org/abs/2401.16427)  
**Date:** 2024-01-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.16427v1/

## Key points

- Fairness in recommender systems has become a significant research focus, with bias and debiasing being key aspects.
- Position bias is a prevalent issue in recommendation algorithms, leading to items at the top of lists receiving more clicks.
- A novel approach using regularization techniques has been proposed to mitigate position bias by incorporating a penalty term.
- The new algorithm has shown superior performance in combating position bias compared to other methods through rigorous experimentation.
- Performance analyses on datasets like MovieLens 1 Million and LDOS-CoMoDa demonstrate the effectiveness of the algorithm in terms of MAE scores and Degree of Matthew Effect metric.
- The algorithm excels particularly in Position Bias Metric Score evaluations, ranking as one of the top performers.
- Future research aims to address other forms of bias such as selection bias and exposure bias to promote fairness within AI algorithms.

## Summary

In the realm of recommender systems, fairness has emerged as a prominent research topic in recent years. One key aspect closely linked to fairness is bias and the process of debiasing. Among the various forms of bias that plague recommendation algorithms, position bias stands out as a prevalent issue. This bias manifests as a tendency for items placed at the top of recommendation lists to receive more clicks compared to those positioned lower down the list. To address this challenge, a novel approach leveraging regularization techniques has been proposed in mitigating the effects of position bias. The method involves incorporating a carefully crafted penalty term aimed at reducing bias while maintaining simplicity and interpretability within the algorithm. Through rigorous experimentation, it has been demonstrated that this new algorithm surpasses other contemporary methods in combating position bias effectively. Furthermore, detailed analyses on datasets such as MovieLens 1 Million and LDOS-CoMoDa showcase the competitive performance of the proposed algorithm in terms of Mean Absolute Error (MAE) scores and Degree of Matthew Effect metric. Notably, when evaluated specifically on Position Bias Metric Score, the algorithm emerges as one of the top performers. Looking ahead, future research endeavors aim to explore solutions for other forms of bias like selection bias and exposure bias with an overarching goal to enhance fairness within AI algorithms. By continuously refining and advancing these methodologies, researchers strive towards creating more equitable and unbiased recommender systems for diverse user populations.