Mitigation of Popularity Bias in Recommendation Systems

Mitigation of Popularity Bias in Recommendation Systems - CEUR-WS

... popularity bias and raise the fairness issue in RSs. Keywords. Popularity bias, Recommender System, Fairness, Mitigation. 1. Introduction. Recommendation ...

(PDF) Mitigation of Popularity Bias In Recommendation Systems

Although improving the overall accuracy, GCNs unfortunately amplify popularity bias -- tail items are less likely to be recommended. This effect ...

To address the popularity bias issues, we develop a gradient-based embedding adjustment approach used in model testing. This strategy is generic ...

EqBal-RS: Mitigating popularity bias in recommender systems

This paper considers an easy-to-understand metric to evaluate the popularity bias as the difference between mean squared error on popular and non-popular items.

Mitigation of Popularity Bias in Recommendation Systems

An empirical analysis of different mitigation techniques for popularity bias is conducted to provide an overview of the present state of the art of ...

Neural_BPR: Multi-processing popularity bias mitigating method in ...

Steck proposed a pre-processing method to mitigate the popularity bias in recommendation systems by scaling the dataset based on popularity. This involves ...

Results show that neither mitigation technique harms the users' satisfaction with the recommendation lists despite promoting underrepresented ...

Mitigation of Popularity Bias in Recommendation Systems

recommendation list. In this work, we conduct an empirical analysis of different mitigation techniques for popularity bias to provide an overview of the present ...

Quantifying and Mitigating Popularity Bias in Conversational ... - arXiv

Abstract:Conversational recommender systems (CRS) have shown great success in accurately capturing a user's current and detailed preference ...

Usually, this mitigation process involves handling a trade-off between predicted item relevance (accuracy) and item popularity. Other strategies ...

Managing Biases in Recommender Systems | by Gabriel Fu - Medium

Popularity bias originates from the model. There are a number of ways to handle it, including using regularization-based method [1] or a list ...

Mitigating Exposure Bias in Recommender Systems

When implicit feedback recommender systems expose users to items, they influence the users' choices and, consequently, their own future ...

Abstract. In this paper, we present the results of an empirical evaluation investigating how recommendation algorithms are affected by popularity bias.

PDF | While recommender systems are highly successful at helping users find relevant information online, they may also exhibit a certain undesired bias.

Evaluating unfairness of popularity bias in recommender systems

The popularity bias problem is one of the most prominent challenges of recommender systems, i.e., while a few heavily rated items receive much attention in ...

It is shown that while calibration methods that aim to match the popularity of the recommended items with popularity preferences of individual users are ...

We considered various recommendation techniques based on the SSL model and compared their impact on popularity bias mitigation measured in terms of Average ...

On Mitigating Popularity Bias in Recommendations via Variational ...

Recommender systems are usually susceptible to Popularity Bias, in the sense that their training procedures have major influence of popular items. This ...

User-centered Evaluation of Popularity Bias in Recommender Systems

In this paper, we show the limitations of the existing metrics to evaluate popularity bias mitigation when we want to assess these algorithms from the users' ...

Large Language Models as Recommender Systems: A Study of ...

We find that the LLM recommender exhibits less popularity bias, even without any explicit mitigation. CCS CONCEPTS. • Information systems → ...