The DOI matches its Crossref record; 26 of the 28 references it cites were found in a registry or an index.
The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
Open accessAbstract
Recently there has been a growing interest in fairness-aware recommender systems including fairness in providing consistent performance across different users or groups of users. A recommender system could be considered unfair if the recommendations do not fairly represent the tastes of a certain group of users while other groups receive recommendations that are consistent with their preferences. In this paper, we use a metric called miscalibration for measuring how a recommendation algorithm is responsive to users’ true preferences and we consider how various algorithms may result in different degrees of miscalibration for different users. In particular, we conjecture that popularity bias which is a well-known phenomenon in recommendation is one important factor leading to miscalibration in recommendation. Our experimental results using two real-world datasets show that there is a connection between how different user groups are affected by algorithmic popularity bias and their level of interest in popular items. Moreover, we show that the more a group is affected by the algorithmic popularity bias, the more their recommendations are miscalibrated.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Current recommendation systems appear to disproportionately amplify high-popularity accounts, creating inequality in exposure that may result in less personalized and miscalibrated recommendations for certain user groups [1].”
From Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X During the 2024 U.S. Presidential Election (2025) · cites this paper as [1]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R. M., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,232
Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135
The MovieLens Datasets: History and Context
Harper, F., & Konstan, J. (2016) · TIIS · doi:10.1145/2827872 · Cited by 5,938
Factorization meets the neighborhood
Koren, Y. (2008) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1401890.1401944 · Cited by 3,959
Resnick, P., & Varian, H. R. (1997) · Communications of the ACM · doi:10.1145/245108.245121 · Cited by 3,772
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501
Evaluating Recommendation Systems
Shani, G., & Gunawardana, A. (2010) · Springer eBooks · doi:10.1007/978-0-387-85820-3_8 · Cited by 1,430
Fairness-Aware Classifier with Prejudice Remover Regularizer
Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33486-3_3 · Cited by 694
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
Adomavičius, G., & Kwon, Y. (2011) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2011.15 · Cited by 674
Fairness of Exposure in Rankings
Singh, A., & Joachims, T. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220088 · Cited by 673
Flagged first, then by citations
2 of the 28 references couldn’t be matched to a known paper.
Research works printed but not linked: 11
- Abdollahpouri Himan, 2019, The Thirty-Second International Flairs Conference.
- Bozdag Engin, 2013, Bias in algorithmic filtering and personalization. Ethics and information technology 15, 3
- Erik Brynjolfsson Yu Jeffrey Hu and Michael D Smith. 2006. From niches to riches: Anatomy of the long tail. Sloan Management Review(2006) 67–71. Erik Brynjolfsson Yu Jeffrey Hu and Michael D Smith. 2006. From niches to riches: Anatomy of the long tail. Sloan Management Review(2006) 67–71.
- Ekstrand D, 2018, Conference on Fairness, Accountability and Transparency. 172–186
- Guo Guibing, 2015, UMAP Workshops.
- Kamishima Toshihiro, 2014, Poster Proceedings of the 8th ACM Conference on Recommender Systems, RecSys 2014
- Liu Weiwen, 2018, 2nd FATRec Workshop held at RecSys
- Mansoury Masoud, 2020, Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems. In The Thirty-Third International Flairs Conference.
- Narayanan Arvind, 2018, Proc. Conf. Fairness Accountability Transp.
- Shani Guy, Recommender systems handbook
- Zafar Muhammad Bilal, 2017, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. arXiv preprint arXiv:1507
Cited by
Sorted by citations
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., Quan, Y., Chang, J., Jin, D., He, X., et al. (2023) · ACM Transactions on Recommender Systems · doi:10.1145/3568022 · Cited by 896
Bias and Debias in Recommender System: A Survey and Future Directions
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3564284 · Cited by 741
Causal Intervention for Leveraging Popularity Bias in Recommendation
Zhang, Y., Feng, F., He, X., Wei, T., Song, C., Ling, G., & Zhang, Y. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462875 · Cited by 443
Fairness in recommender systems: research landscape and future directions
Deldjoo, Y., Jannach, D., Bellogín, A., Difonzo, A., & Zanzonelli, D. (2023) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-023-09364-z · Cited by 290
A survey on popularity bias in recommender systems
Klimashevskaia, A., Jannach, D., Elahi, M., & Trattner, C. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09406-0 · Cited by 223
Popularity Bias is not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Zhao, Z., Chen, J., Zhou, S., He, X., Cao, X., Zhang, F., & Wu, W. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3218994 · Cited by 117
“It would decrease serendipity [25], [26], [27] and fairness [13], [28], [29], [30] of recommendation results, hurting user experience and causing customer churn.”
BackgroundCausal Representation Learning for Out-of-Distribution Recommendation
Wang, W.-J., Lin, X.-Y., Feng, F.-L., He, X.-N., Lin, M., & Chua, T.-S. (2022) · The Web Conference · doi:10.1145/3485447.3512251 · Cited by 157
Fairness in Recommendation: Foundations, Methods, and Applications
Li, Y., Chen, H., Xu, S., Ge, Y., Tan, J., Liu, S., & Zhang, Y. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3610302 · Cited by 183
A survey on fairness-aware recommender systems
Jin, D., Wang, L., Zhang, H., Zheng, Y., Ding, W., Xia, F., & Pan, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101906 · Cited by 125
Break the Loop: Gender Imbalance in Music Recommenders
Ferraro, A., Serra, X., & Bauer, C. (2021) · Proceedings of the 2021 Conference on Human Information Interaction and Retrieval · doi:10.1145/3406522.3446033 · Cited by 92
(2024) · ACM eBooks · doi:10.1145/3674127 · Cited by 86
Matching Algorithms: Fundamentals, Applications and Challenges
Ren, J. F., Xia, F., Chen, X., Liu, J., Hou, M., Shehzad, A. H., Sultanova, N., & Kong, X. (2021) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2021.3067655 · Cited by 90
Wang, J., Zeng, Z., Wang, Y., Wang, Y., Lu, X., Li, T., Yuan, J., Zhang, R., Zheng, H.-T., & Xia, S. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3611967 · Cited by 64
Recommender systems effect on the evolution of users’ choices distribution
Hazrati, N., & Ricci⋆, F. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102766 · Cited by 56
FairGAN: GANs-based Fairness-aware Learning for Recommendations with Implicit Feedback
Li, J., Ren, Y., & Deng, K. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3511958 · Cited by 59
Exploiting personalized calibration and metrics for fairness recommendation
da Silva, D. C., Manzato, M. G., & Dur�ão, F. A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115112 · Cited by 54
Deconfounded Causal Collaborative Filtering
Xu, S., Tan, J., Heinecke, S., Li, V. J., & Zhang, Y. (2023) · ACM Transactions on Recommender Systems · doi:10.1145/3606035 · Cited by 53
Countering Popularity Bias by Regularizing Score Differences
Rhee, W., Cho, S., & Suh, B. (2022) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3523227.3546757 · Cited by 41
Zanon, A. L., da Rocha, L. C. D., & Manzato, M. G. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109333 · Cited by 36
Mitigating Popularity Bias for Users and Items with Fairness-centric Adaptive Recommendation
Liu, Z., Fang, Y., & Wu, M. (2022) · ACM Transactions on Information Systems · doi:10.1145/3564286 · Cited by 34
Mitigating Sentiment Bias for Recommender Systems
Lin, C., Liu, X., Xv, G., & Li, H. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462943 · Cited by 36
Shi, X., Liu, Q., Xie, H. L., Wu, D., Peng, B., Shang, M., & Lian, D. (2023) · ACM Transactions on Information Systems · doi:10.1145/3618107 · Cited by 32
Debiasing Recommendation with Personal Popularity
Ning, W., Cheng, R. C. K., Yan, X., Kao, B., Huo, N., Haldar, N. A. H., & Tang, B. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645421 · Cited by 29
Popularity Bias in Recommender Systems: The Search for Fairness in the Long Tail
Carnovalini, F., Rodà, A., & Wiggins, G. A. (2025) · Information · doi:10.3390/info16020151 · Cited by 35
Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions.Preprint
Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., Quan, Y., Chang, J., Jin, D., He, X., et al. (2021) · arXiv (Cornell University) · Cited by 27
Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness
Neophytou, N., Mitra, B., & Stinson, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-99736-6_43 · Cited by 36
Deconfounded recommendation via causal intervention
Yu, D., Li, Q., Wang, X., & Xu, G. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.089 · Cited by 27
How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
Lin, S., Gao, C., Chen, J., Zhou, S., Hu, B., Feng, Y., Chen, C., & Wang, C. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3703579 · Cited by 29
Context- and Fairness-Aware In-Process Crowdworker Recommendation
Wang, J., Yang, Y., Wang, S., Hu, J., & Wang, Q. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3487571 · Cited by 24
Neighbor Library-Aware Graph Neural Network for Third Party Library Recommendation
Jin, Y., Zhang, Y. J., & Zhang, Y. (2023) · Tsinghua Science & Technology · doi:10.26599/tst.2022.9010042 · Cited by 23
Longitudinal Impact of Preference Biases on Recommender Systems’ Performance
Zhou, M., Zhang, J., & Adomavičius, G. (2023) · Information Systems Research · doi:10.1287/isre.2021.0133 · Cited by 25
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank RecommendationPreprint
Mansoury, M., Abdollahpouri, H., Mobasher, B., Pechenizkiy, M., Burke, R., & Sabouri, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.03440 · Cited by 29
Self-citationChizari, N., Shoeibi, N., & Garcı́a, M. N. M. (2022) · Electronics · doi:10.3390/electronics11203301 · Cited by 21
Simulating Users’ Interactions with Recommender Systems
Hazrati, N., & Ricci⋆, F. (2022) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3511047.3536402 · Cited by 19
Alleviating Video-length Effect for Micro-video Recommendation
Quan, Y., Ding, J., Gao, C., Li, N., Yi, L., Jin, D., & Li, Y. (2023) · ACM Transactions on Information Systems · doi:10.1145/3617826 · Cited by 20
Ferwerda, B., Ingesson, E., Berndl, M., & Schedl, M. (2023) · Conference on Human Information Interaction and Retrieval · doi:10.1145/3576840.3578287 · Cited by 18
Popularity-aware sequential recommendation with user desire
Wu, J., Yang, B., Mao, R., & Li, Q. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121429 · Cited by 20
The Impact of Differential Privacy on Recommendation Accuracy and Popularity Bias
Müllner, P., Lex, E., Schedl, M., & Kowald, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56066-8_33 · Cited by 18
User Perception of Fairness-Calibrated Recommendations
Alves, G., Jannach, D., Souza, R., & Manzato, M. G. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3627043.3659558 · Cited by 22
Causal embedding of user interest and conformity for long-tail session-based recommendations
Zeyu, H., Yan, L., Feng, W., Zhang, W., Alenezi, F. S., & Tiwari, P. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119167 · Cited by 17
Recommend Me? Designing Fairness Metrics with Providers
Smith, J. J., Satwani, A., Burke, R., & Fiesler, C. L. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659044 · Cited by 17
Self-citationYe, J., Luceri, L., & Ferrara, E. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732159 · Cited by 16
“Current recommendation systems appear to disproportionately amplify high-popularity accounts, creating inequality in exposure that may result in less personalized and miscalibrated recommendations for certain user groups [1].”
Long-tail session-based recommendation from calibration
Chen, J., Wu, W., Shi, L., Zheng, W., & He, L. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03718-7 · Cited by 16
Recommender Systems Meet Large Language Model Agents: A Survey
Zhu, X., Wang, Y., Gao, H., Xu, W., Wang, C., Liu, Z., Wang, K., Jin, M., Pang, L. X., Weng, Q., et al. (2025) · Foundations and Trends® in Privacy and Security · doi:10.1561/3300000050 · Cited by 26
A Study on Accuracy, Miscalibration, and Popularity Bias in Recommendations
Kowald, D., Mayr, G., Schedl, M., & Lex, E. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-37249-0_1 · Cited by 15
Cluster Anchor Regularization to Alleviate Popularity Bias in Recommender Systems
Chang, B., Meng, C., Ma, H., Chang, S., Gu, Y., Peng, Y., Feng, J., Zhang, Y., Bi, S., Ed, H., et al. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589335.3648312 · Cited by 16
Zipf Matrix Factorization: Matrix Factorization with Matthew Effect Reduction
Wang, H. (2021) · International Conference on Artificial Intelligence and Big Data (ICAIBD) · doi:10.1109/icaibd51990.2021.9459018 · Cited by 14
Breaking the routine: spatial hypertext concepts for active decision making in recommender systems
Atzenbeck, C., Herder, E., & Roßner, D. (2023) · New Review of Hypermedia and Multimedia · doi:10.1080/13614568.2023.2170474 · Cited by 14
Fairness-Aware Recommendation in Multi-Sided Platforms
Mansoury, M. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441672 · Cited by 14
Self-citationOur Model Achieves Excellent Performance on MovieLens: What Does It Mean?
Fan, Y.-C., Ji, Y., Zhang, J., & Sun, A. (2024) · ACM Transactions on Information Systems · doi:10.1145/3675163 · Cited by 13
A hybrid group-based movie recommendation framework with overlapping memberships
Ali, Y., Khalid, O., Khan, I. A., Hussain, S. S., Rehman, F. U., Siraj, S., & Nawaz, R. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266103 · Cited by 13
Towards Confidence-aware Calibrated Recommendation
Naghiaei, M., Rahmani, H. A., Aliannejadi, M., & Sonboli, N. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557713 · Cited by 13
EqBal-RS: Mitigating popularity bias in recommender systems
Gupta, S., Kaur, K., & Jain, S. (2023) · Journal of Intelligent Information Systems · doi:10.1007/s10844-023-00817-w · Cited by 14
Understanding and mitigating multi-sided exposure bias in recommender systems
Mansoury, M. (2022) · ACM SIGWEB Newsletter · doi:10.1145/3566100.3566103 · Cited by 13
Self-citationChen, Z., Piao, J., Lan, X., Cao, H., Gao, C., Lu, Z., & Li, Y. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555646 · Cited by 15
COSTA: Contrastive Spatial and Temporal Debiasing framework for next POI recommendation
Lei, Y., Shen, L., Sun, Z., He, T., Feng, S., & Liu, G. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107212 · Cited by 11
Towards a Causal Decision-Making Framework for Recommender Systems
Cavenaghi, E., Zanga, A., Stella, F. A., & Zanker, M. (2023) · ACM Transactions on Recommender Systems · doi:10.1145/3629169 · Cited by 12
Putting Popularity Bias Mitigation to the Test: A User-Centric Evaluation in Music Recommenders
Ungruh, R., Dinnissen, K., Volk, A., Pera, M. S., & Hauptmann, H. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688102 · Cited by 11
Bringing Friends into the Loop of Recommender Systems: An Exploratory Study
Piao, J., Zhang, G., Xu, F., Chen, Z., Zheng, Y., Gao, C., & Li, Y. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479583 · Cited by 11
Introducing a framework and a decision protocol to calibrated recommender systems
da Silva, D. C., & Dur�ão, F. A. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04681-7 · Cited by 10
Dual disentanglement of user–item interaction for recommendation with causal embedding
Wang, C., Ye, Y., Ma, L., Li, D., & Zhuang, L. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103456 · Cited by 11
A Novel Popularity Extraction Method Applied in Session-Based Recommendation
Peng, Y., Xu, S., Chen, Q., Huang, W., & Huang, Y. (2024) · Tsinghua Science & Technology · doi:10.26599/tst.2023.9010061 · Cited by 10
Balanced Quality Score: Measuring Popularity Debiasing in Recommendation
Coppolillo, E., Minici, M., Ritacco, E., Caroprese, L., Pisani, F. S., & Manco, G. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3650043 · Cited by 10
Novel approaches to measuring the popularity inclination of users for the popularity bias problem
Tacli, Y., Yalçın, E., & Bilge, A. (2022) · International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) · doi:10.1109/ismsit56059.2022.9932663 · Cited by 10
Algorithmic Discrimination and Privacy Protection
Falletti, E. (2023) · Journal of Digital Technologies and Law · doi:10.21202/jdtl.2023.16 · Cited by 19
Castells, P., & Jannach, D. (2024) · ACM eBooks · doi:10.1145/3674127.3674131 · Cited by 10
The Idiosyncratic Effects of Adversarial Training on Bias in Personalized Recommendation Learning
Anelli, V. W., Noia, T. D., & Merra, F. A. (2021) · ACM Conference on Recommender Systems · doi:10.1145/3460231.3478858 · Cited by 9
MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems
Ko, Y., Yu, J.-S., Bae, H.-K., Park, Y., Lee, D., & Kim, S. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00039 · Cited by 9
Robustness of privacy-preserving collaborative recommenders against popularity bias problem
Gulsoy, M., Yalçın, E., & Bilge, A. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1438 · Cited by 8
Calibrated Recommendations for Users with Decaying Attention
Kleinberg, J. M., Ryu, E., & Eva Tardos, D. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71033-9_25 · Cited by 8
AMBAR: A dataset for Assessing Multiple Beyond-Accuracy Recommenders
Gómez, E., Contreras, D., Boratto, L., & Salamó, M. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688067 · Cited by 9
Computational Versus Perceived Popularity Miscalibration in Recommender Systems
Lesota, O., Escobedo, G., Deldjoo, Y., Ferwerda, B., Kopeinik, S., Lex, E., Rekabsaz, N., & Schedl, M. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591964 · Cited by 7
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
Chen, J., Wu, W., Shi, L., Yu, J., Hu, W., Chen, X., Zheng, W., & He, L. (2022) · Applied Sciences · doi:10.3390/app122211765 · Cited by 7
Essay, P., & Rajasekharan, A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26770 · Cited by 7
Recommender systems, ground truth, and preference pollution
Adomavičius, G., Bockstedt, J. C., Curley, S. P., & Zhang, J. (2022) · AI Magazine · doi:10.1002/aaai.12055 · Cited by 9
Robustness Against Polarity Bias in Decoupled Group Recommendations Evaluation
Dokoupil, P., & Peška, L. (2022) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3511047.3537650 · Cited by 7
Toward personalized AI-powered recommender systems to support users’ daily music choice experiences
Shin, Y., Ranjan, K., Kowalski, M. C., & Yoon, J. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2025.103574 · Cited by 6
Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation
Lin, K., Mansoury, M., Eskandanian, F., Sabouri, M., & Mobasher, B. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3631700.3664869 · Cited by 5
Self-citationFine-tuning for Inference-efficient Calibrated Recommendations
Lesota, O., Bajko, A., Walder, M., Wenzel, M., Tommasel, A., & Schedl, M. (2025) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3705328.3759319 · Cited by 5
Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets
Gaeta, R., Garetto, M., Ruffo, G., & Flammini, A. (2022) · ACM Transactions on Information Systems · doi:10.1145/3530790 · Cited by 6
Ahn, Y., & Lin, Y. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3650044 · Cited by 5
MIN: Multi-stage Interactive Network for Multimodal Recommendation
Mo, M., Lu, W., Xie, Q., Lv, X., Xiao, Z., Yang, H., & Zhang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0570-5_14 · Cited by 5
Recommendation Fairness: From Static to DynamicPreprint
Zhang, D., & Wang, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.03150 · Cited by 15
Collaborative filtering algorithms are prone to mainstream-taste bias
Analytis, P. P., & Hager, P. (2023) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3604915.3608825 · Cited by 4
MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels
Gómez, E., Contreras, D., Boratto, L., & Salamó, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56027-9_2 · Cited by 4
DeCoRec: Decoupled Collaborative Refinement for Multi-Modal Sequential Recommendations
Chen, Z., Xu, W., Zhang, Y., Hou, Y., Wen, Z., & Wang, C. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755475 · Cited by 4
A Re-ranking Approach for Two-sided Fairness on Recommendation Systems
Peng, Y., Qian, X., & Song, W. (2023) · International Conference on Computing, Networks and Internet of Things (CNIOT) · doi:10.1145/3603781.3603836 · Cited by 4
Fairness Through Domain Awareness: Mitigating Popularity Bias for Music Discovery
Salganik, R., Díaz, F., & Farnadi, G. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56066-8_27 · Cited by 4
Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures
Kolb, T. E. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688027 · Cited by 5
Uma Abordagem em Etapa de Processamento para Redução do Viés de Popularidade
de Souza, R. F., & Manzato, M. G. (2024) · Brazilian Symposium on Multimedia and the Web · doi:10.5753/webmedia.2024.241542 · Cited by 4
Trippas, J. R. (2024) · ACM eBooks · doi:10.1145/3674127.3674135 · Cited by 5
Automatic Feature Fairness in Recommendation via Adversaries
Hu, H., Cao, Y., He, Z., Tan, S., & Kan, M. (2023) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3624918.3625318 · Cited by 4
Popularity-Debiased Graph Self-Supervised for Recommendation
Li, S., Hu, X., Guo, J., Bin, L. I. U., Qi, M., & Jia, Y. (2024) · Electronics · doi:10.3390/electronics13040677 · Cited by 4
Rank-sensitive proportional aggregations in dynamic recommendation scenarios
Balcar, Š., Škrhák, V., & Peška, L. (2022) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-021-09311-w · Cited by 4
A Survey on Fairness-Aware Recommender SystemsPreprint
Jin, D., Wang, L., Zhang, H., Zheng, Y., Ding, W., Xia, F., & Pan, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4469569 · Cited by 4
Effects of the spiral of silence on minority groups in recommender systems
Khan, S. N., & Herder, E. (2023) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3603163.3609041 · Cited by 3
Causal intervention for knowledge graph denoising in recommender systems
Guo, Z., Song, P., Feng, C., Yao, K., Dang, C., & Liang, J. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02500-0 · Cited by 3
Price-aware debiased learning model for recommendation
Wu, J., Yang, B., Zhu, Q., Mao, R., & Li, Q. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107872 · Cited by 3
Integrating Individual and Group Fairness for Recommender Systems through Social Choice
Aird, A., Štefancová, E., Buhayh, A., All, C., Homola, M., Mattei, N., & Burke, R. (2025) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3705328.3748087 · Cited by 3
Self-citationCalibrated Recommendations: Survey and Future Directions
da Silva, D. C., & Jannach, D. (2026) · ACM Transactions on Recommender Systems · doi:10.1145/3789266 · Cited by 3
Metadata
- Authors
- Abdollahpouri, H., Mansoury, M., Burke, R., & Mobasher, B.
- Affiliations
- University of Colorado Boulder (US)
- Eindhoven University of Technology (NL)
- DePaul University (US)
- Venue
- Fourteenth ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2020-09-22
- Pages
- 726-731
- OpenAlex
- W3080925373
- Cited by
- 183
- References
- 28
- Citations by year
- 2026: 24 · 2025: 27 · 2024: 56 · 2023: 34 · 2022: 25 · 2021: 17
- FWCI
- 14.41
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM Conference on Recommender Systems (RecSys) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National science foundation (1911025)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Keywords
- popularity bias, calibration error, recommendation fairness, recommender systems, user preference representation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
