The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation

Rahmani, Hossein A.; Deldjoo, Yashar; Tourani, Ali; Naghiaei, Mohammadmehdi

doi:10.1007/978-3-031-09316-6_6

Cited by 10 publications

(8 citation statements)

References 28 publications

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“…Chakraborty et al [11] claimed that all providers should receive the amount of exposures proportional to their relevance in economy platforms. Rahmani et al [38] studied the trade-offs between the user and producer fairness in Point-of-Interest recommendations. TFROM [47] and CPFair [32] formulated the trade-off as a knapsack problem and a relaxed linear programming problem, respectively.…”

Section: Related Workmentioning

confidence: 99%

P-MMF: Provider Max-min Fairness Re-ranking in Recommender System

Chen

et al. 2023

Proceedings of the ACM Web Conference 2023

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Section: Related Workmentioning

confidence: 99%

P-MMF: Provider Max-min Fairness Re-ranking in Recommender System

Chen

et al. 2023

Proceedings of the ACM Web Conference 2023

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“…12 One important question in such research works is if certain groups of users-in particular niche item lovers-receive less utility from the recommendations than others. In a number of works such phenomena are seen as a form of potential discrimination, leading to questions of fairness in recommender systems and its relationship to popularity bias [7,25,85,86,102,111].…”

Section: Bias Quantification Approachesmentioning

confidence: 99%

“…Furthermore, we identified a number of research works where there was no detailed motivation provided in the papers on why popularity bias should be [111] User Popularity Deviation (UPD), temporal version of UPD [59,83] Tail Item Prediction Quality -Popularity-Rank Correlation (item-or user-based) [151] Popularity Biasedness [55] mitigated at all, i.e., which kinds of harm one seeks to avoid. In addition, no explanation is often provided on how the authors derived when a bias mitigation procedure is successful.…”

Section: Definition Applications and Datasetsmentioning

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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“…Considering both consumers' and providers' perspectives, Chakraborty et al [12] present mechanisms for CP-fairness in matching platforms such as Airbnb and Uber. Rahmani et al [46] studied the interplays and tradeoffs between consumer and producer fairness in Point-of-Interest recommendations. Patro et al [43] map fair recommendation problem to the constrained version of the fair allocation problem with indivisible goods and propose an algorithm to recommend top-K items by accounting for producer fairness aspects.…”

Section: Background and Related Workmentioning

confidence: 99%