Popularity Bias in Recommender Systems - A Review

Ahanger, Abdul Basit; Aalam, Syed Wajid; Bhat, Muzafar Rasool; Assad, Assif

doi:10.1007/978-3-031-07012-9_37

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…In other words, bias can result in the limitation of the users' choices and contaminate users' feedback, which can amplify exposure bias [41]. Among the mentioned types of biases, popularity bias has been considered the most important type in the field of RSs [42].…”

Section: Bias In Rssmentioning

confidence: 99%

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A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

2022

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Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload. Currently, recommendation methods based on deep learning are gaining ground over traditional methods such as matrix factorization due to their ability to represent the complex relationships between users and items and to incorporate additional information. The fact that these data have a graph structure and the greater capability of Graph Neural Networks (GNNs) to learn from these structures has led to their successful incorporation into recommender systems. However, the bias amplification issue needs to be investigated while using these algorithms. Bias results in unfair decisions, which can negatively affect the company’s reputation and financial status due to societal disappointment and environmental harm. In this paper, we aim to comprehensively study this problem through a literature review and an analysis of the behavior against biases of different GNN-based algorithms compared to state-of-the-art methods. We also intend to explore appropriate solutions to tackle this issue with the least possible impact on the model’s performance.

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Section: Bias In Rssmentioning

confidence: 99%

“…Aggregation bias can happen due to the wrong assumptions about the effects of population on the model's results. Popularity occurs because of more popular items gaining more interactions [42,44]. Algorithmic bias can happen due to technical issues inside the used algorithm.…”

Section: Bias In Rssmentioning

confidence: 99%

A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

2022

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“…To our knowledge, the recent conference paper by Ahanger et al [14] is the only work that exclusively focuses on popularity biases in recommender systems. In their paper, the authors report the technical details of a selected set of recent algorithmic approaches to mitigate popularity biases.…”

Section: An Impact-oriented Definition Of Popularity Bias and Itsmentioning

confidence: 99%

“…We note here that the assignment of individual papers to certain categories in certain cases is subject to a certain level of interpretation, in particular when it comes to distinguishing between in-processing and postprocessing approaches. 14 The categorization of individual papers can be found in Table 2, where one paper can also be assigned to more than one category. In the following, we review selected approaches from the different categories.…”

Section: Bias Mitigation Approachesmentioning

confidence: 99%

Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches

Klimashevskaia

Elahi

Jannach

et al. 2022

Communications in Computer and Information Science

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Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesserknown items in a catalogue. Existing research, however, suggests that in many situations today's recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and we review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey therefore includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. We furthermore critically discuss today's literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.

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“…Those segments of the population that suffer the negative consequences of bias are the so-called protected groups on which mitigation strategies should focus. All of these facts have driven the research currently being carried out in this field [5,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems

2023

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In today’s technology-driven society, many decisions are made based on the results provided by machine learning algorithms. It is widely known that the models generated by such algorithms may present biases that lead to unfair decisions for some segments of the population, such as minority or marginalized groups. Hence, there is concern about the detection and mitigation of these biases, which may increase the discriminatory treatments of some demographic groups. Recommender systems, used today by millions of users, are not exempt from this drawback. The influence of these systems on so many user decisions, which in turn are taken as the basis for future recommendations, contributes to exacerbating this problem. Furthermore, there is evidence that some of the most recent and successful recommendation methods, such as those based on graphical neural networks (GNNs), are more sensitive to bias. The evaluation approaches of some of these biases, as those involving protected demographic groups, may not be suitable for recommender systems since their results are the preferences of the users and these do not necessarily have to be the same for the different groups. Other assessment metrics are aimed at evaluating biases that have no impact on the user. In this work, the suitability of different user-centered bias metrics in the context of GNN-based recommender systems are analyzed, as well as the response of recommendation methods with respect to the different types of biases to which these measures are addressed.

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Popularity Bias in Recommender Systems - A Review

Cited by 8 publications

References 25 publications

A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches

Bias Assessment Approaches for Addressing User-Centered Fairness in GNN-Based Recommender Systems

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