Popularity Bias is not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Open accessAbstract
Recommender system usually suffers from severepopularity bias— the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such skewed distribution may result from the users’conformityto the group, which deviates from reflecting users’ true preference. Existing efforts for tackling this issue mainly focus on completely eliminating popularity bias. However, we argue that not all popularity bias is evil. Popularity bias not only results from conformity but alsoitem quality, which is usually ignored by existing methods. Some items exhibit higher popularity as they have intrinsic better property. Blindly removing the popularity bias would lose such important signal, and further deteriorate model performance. To sufficiently exploit such important information for recommendation, it is essential to disentangle the benign popularity bias caused by item quality from the harmful popularity bias caused by conformity. Although important, it is quite challenging as we lack an explicit signal to differentiate the two factors of popularity bias. In this paper, we propose to leverage temporal information as the two factors exhibit quite different patterns along the time: item quality revealing item inherent property is stable and static while conformity that depends on items’ recent clicks is highly time-sensitive. Correspondingly, we further propose a novelTime-awareDisEntangled framework (TIDE), where a click is generated from three components namely the static item quality, the dynamic conformity effect, as well as the user-item matching score returned by any recommendation model. Lastly, we conduct interventional inference so that the recommendation can benefit from the benign popularity bias while circumvent the harmful one. Extensive experiments on four real-world datasets demonstrated the effectiveness of TIDE.
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An appropriate proportion of popularity bias in the system can improve performance and user satisfaction.
“An appropriate proportion of popularity bias in the system can improve performance and user satisfaction [183].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [183]
The Introduction and §2.1 say that popularity bias arising from item quality can benefit recommendations, while bias arising from conformity is harmful; the Popularity Bias in recommendation section says leveraging the quality signal can boost performance. §2.2 treats higher ratings as suggesting greater satisfaction with an item, but the paper does not establish that a particular proportion of popularity bias improves user satisfaction. Its proposed distinction is between benign and harmful sources of bias, not an optimal amount of bias. (LLM, conf=0.84)
Experimental — accuracy not yet measuredPopularity bias can act as a double-edged sword.
“Moreover, popularity bias can act as a double-edged sword [182, 183].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [183]
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Cites
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
Neural Collaborative Filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052569 · Cited by 7,066 · cited 1× · see citing works
“experiments, and it would be straightforward to replace it with more sophisticated models such as Factorization Machine [15], or Neural Network [5], [16].”
MethodSelf-citationHe, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401063 · Cited by 5,083 · cited 4× · see citing works
“Amazon-CDs is increased to 8,192 for speed and the number of convolutional layers is searched in {2, 3, 4} as advised by [5].”
BackgroundMethodInfluentialSelf-citationBPR: Bayesian Personalized Ranking from Implicit FeedbackPreprint
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2618 · Cited by 4,604 · see citing works
Pearson Correlation Coefficient
Benesty, J., Chen, J., Huang, Y. A., & Cohen, I. (2009) · Springer topics in signal processing · doi:10.1007/978-3-642-00296-0_5 · Cited by 3,540 · see citing works
Self-citationThe long tail: why the future of business is selling less of more
(2007) · Choice Reviews Online · doi:10.5860/choice.44-2783 · Cited by 2,869 · see citing works
Deep Interest Network for Click-Through Rate Prediction
Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., Yan, Y., Jin, J., Li, H., & Gai, K. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219823 · Cited by 2,128 · cited 2× · see citing works
“(3) ( U, I ) → M → Y : these links project user and item features ( e.g., IDs) into their matching scores m ui = f θ ( u, i ) . f θ ( u, i ) can be implemented by various recommendation models, such as MF [4], LightGCN [5], DIN [6], etc. Finally these three components are aggregated into a final prediction score for r…”
BackgroundFactorization Machines with libFM
Rendle, S. (2012) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2168752.2168771 · Cited by 1,345 · cited 1× · see citing works
“Matrix Factorization (MF) has been selected as the main backbone recommendation model for experiments, and it would be straightforward to replace it with more sophisticated models such as Factorization Machine [15], or Neural Network [5], [16].”
MethodBias 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 · see citing works
Self-citationDisentangled Graph Collaborative Filtering
Wang, X., Jin, H., Zhang, A., et al. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401137 · Cited by 705 · see citing works
Self-citationUnbiased Learning-to-Rank with Biased Feedback
Joachims, T., Swaminathan, A., & Schnabel, T. (2017) · Proceedings of the Tenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3018661.3018699 · Cited by 531 · cited 3× · see citing works
“MF-IPS [10], [11]: a classic strategy for eliminating popularity bias by re-weighting each instance according to item popularity.”
BackgroundInfluentialCounterfactual reasoning and learning systems: the example of computational advertising
Bottou, L., Peters, J., Quiñonero-Candela, J., Charles, D. X., Chickering, D. M., Portugaly, E., Ray, D., Simard, P., & Snelson, E. (2013) · Cited by 497 · see citing works
The long tail of recommender systems and how to leverage it
Park, Y., & Tuzhilin, A. (2008) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/1454008.1454012 · Cited by 444 · cited 1× · see citing works
“Worse still, recommendation model not only inherits the bias, but also amplifies bias, making the popular items dominate the top recommendations [13], [19], [20], [21], [22], [23].”
BackgroundCausal 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 · cited 10× · see citing works
“As recent work [2], here we simply use a uniform”
BackgroundMethodInfluentialSelf-citationModeling User Exposure in Recommendation
Liang, D., Charlin, L., McInerney, J., & Blei, D. M. (2016) · International World Wide Web Conference (WWW) · doi:10.1145/2872427.2883090 · Cited by 368 · cited 1× · see citing works
“Besides popularity bias, recent works have studied other types of biases in recommendation including: Selection bias, which happens as users are free to choose which items to rate, so that the observed ratings are not a representative sample of all ratings [36], [37], [38], [39]; Exposure bias, which happens in implic…”
BackgroundDisentangling User Interest and Conformity for Recommendation with Causal Embedding
Zheng, Y., Gao, C., Li, X., He, X., Li, Y., & Jin, D. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449788 · Cited by 356 · see citing works
Self-citationHow algorithmic confounding in recommendation systems increases homogeneity and decreases utility
Chaney, A. J. B., Stewart, B., & Engelhardt, B. E. (2018) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3240323.3240370 · Cited by 345 · cited 1× · see citing works
“It would decrease serendipity [25], [26], [27] and fairness [13], [28], [29], [30] of recommendation results, hurting user experience and causing customer churn.”
BackgroundModel-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System
Wei, T., Feng, F., Chen, J., Wu, Z., Yi, J., & He, X. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467289 · Cited by 325 · cited 4× · see citing works
“most successful and representative strategy [2], [7], [8].”
BackgroundMethodInfluentialSelf-citationCollaborative prediction and ranking with non-random missing data
Marlin, B. M., & Zemel, R. S. (2009) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/1639714.1639717 · Cited by 319 · cited 1× · see citing works
“Besides popularity bias, recent works have studied other types of biases in recommendation including: Selection bias, which happens as users are free to choose which items to rate, so that the observed ratings are not a representative sample of all ratings [36], [37], [38], [39]; Exposure bias, which happens in implic…”
BackgroundRecommendations as Treatments: Debiasing Learning and EvaluationPreprint
Schnabel, T., Swaminathan, A., Singh, A., Chandak, N., & Joachims, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.05352 · Cited by 296 · see citing works
Causal embeddings for recommendation
Bonner, S., & Vasile, F. (2018) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3240323.3240360 · Cited by 275 · cited 2× · see citing works
“[33] leverage regularization to distill knowledge from the uniform data for addressing popularity bias.”
BackgroundFrom Niches to Riches: The Anatomy of the Long Tail
Brynjolfsson, E., Hu, Y. J., & Smith, M. D. (2018) · Figshare · doi:10.1184/r1/6471422 · Cited by 250 · see citing works
Feedback Loop and Bias Amplification in Recommender Systems
Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B., & Burke, R. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412152 · Cited by 240 · cited 1× · see citing works
“but also amplifies bias, making the popular items dominate the top recommendations [13], [19], [20], [21], [22], [23].”
BackgroundLearning Disentangled Representations for RecommendationPreprint
Ma, J., Zhou, C., Cui, P., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.14238 · Cited by 197 · cited 1× · see citing works
“[45] leverage Variational Auto-Encoder [46] to disentangle high-level concepts associated with user intentions as well as low-level factors (e.”
BackgroundThe Connection Between Popularity Bias, Calibration, and Fairness in Recommendation
Abdollahpouri, H., Mansoury, M., Burke, R., & Mobasher, B. (2020) · Fourteenth ACM Conference on Recommender Systems · doi:10.1145/3383313.3418487 · Cited by 183 · cited 1× · see citing works
“It would decrease serendipity [25], [26], [27] and fairness [13], [28], [29], [30] of recommendation results, hurting user experience and causing customer churn.”
BackgroundCelma, Ò., & Cano, P. (2008) · doi:10.1145/1722149.1722154 · Cited by 170 · cited 1× · see citing works
“Worse still, recommendation model not only inherits the bias, but also amplifies bias, making the popular items dominate the top recommendations [13], [19], [20], [21], [22], [23].”
BackgroundUnderstanding Echo Chambers in E-commerce Recommender Systems
Ge, Y., Zhao, S., Zhou, H., Pei, C., Sun, F., Ou, W., & Zhang, Y. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401431 · Cited by 158 · see citing works
Offline Evaluation to Make Decisions About PlaylistRecommendation Algorithms
Gruson, A., Chandar, P., Charbuillet, C., McInerney, J., Hansen, S., Tardieu, D., & Carterette, B. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291027 · Cited by 149 · cited 1× · see citing works
“classified into four types: (1) Inverse propensity scoring (IPS) [11], [31] is a classic strategy that directly adjust the data distribution with re-weighting each instance according”
BackgroundPopularity-Opportunity Bias in Collaborative Filtering
Zhu, Z., He, Y., Zhao, X., Zhang⋆, Y., Wang, J., & Caverlee, J. (2021) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3437963.3441820 · Cited by 147 · cited 2× · see citing works
“As PDA demonstrates superior performance over ranking-based methods [13], [14], we do not include these methods as baselines.”
MethodManaging Popularity Bias in Recommender Systems with Personalized Re-rankingPreprint
Abdollahpouri, H., Burke, R., & Mobasher, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.07555 · Cited by 132 · cited 4× · see citing works
“but also amplifies bias, making the popular items dominate the top recommendations [13], [19], [20], [21], [22], [23].”
BackgroundMethodInfluentialCañamares, R., & Castells, P. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210014 · Cited by 125 · cited 1× · see citing works
“For example, some work [7], [18] argues that such skewed distribution may be caused by user conformity, deviating from reflecting”
BackgroundThe Unfairness of Popularity Bias in RecommendationPreprint
Abdollahpouri, H., Mansoury, M., Burke, R., & Mobasher, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.13286 · Cited by 125 · cited 1× · see citing works
“It would decrease serendipity [25], [26], [27] and fairness [13], [28], [29], [30] of recommendation results, hurting user experience and causing customer churn.”
BackgroundNovel Recommendation Based on Personal Popularity Tendency
Oh, J., Park, S., Yu, H., Song, M., & Park, S.-T. (2011) · Proceedings · doi:10.1109/icdm.2011.110 · Cited by 103 · cited 1× · see citing works
“(3) Regularization has been introduced by some researchers to push the model towards balanced recommendation [32], [33], [34], [35].”
BackgroundCorrecting for Selection Bias in Learning-to-rank Systems
Ovaisi, Z., Ahsan, R., Zhang, Y., Vasilaky, K., & Zheleva, E. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380255 · Cited by 103 · see citing works
Chen, Z., Xiao, R., Li, C., Ye, G., Sun, H., & Deng, H. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401043 · Cited by 103 · cited 1× · see citing works
“(3) Regularization has been introduced by some researchers to push the model towards balanced recommendation [32], [33], [34],”
BackgroundProbabilistic Matrix Factorization with Non-random Missing Data
Hernández-Lobato, J. M., Houlsby, N., & Zoubin Ghahramani, A. (2014) · Cited by 101 · see citing works
On a Robust Correlation Coefficient
Abdullah, M. B. (1990) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2349088 · Cited by 98 · cited 5× · see citing works
“We calculate Kendall Tau Ranking Correlation Coefficient (RCC) [17] between the item lists ranked by them.”
MethodInfluentialAlgorithmic Glass Ceiling in Social Networks
Stoica, A.-A., Riederer, C., & Chaintreau, A. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3178876.3186140 · Cited by 90 · cited 1× · see citing works
“Besides popularity bias, recent works have studied other types of biases in recommendation including: Selection bias, which happens as users are free to choose which items to rate, so that the observed ratings are not a representative sample of all ratings [36], [37], [38], [39]; Exposure bias, which happens in implic…”
BackgroundExploring author gender in book rating and recommendation
Ekstrand, M. D., & Kluver, D. (2021) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-020-09284-2 · Cited by 66 · see citing works
Correcting Popularity Bias by Enhancing Recommendation Neutrality.
Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2014) · Cited by 66 · see citing works
Multi-sided Exposure Bias in RecommendationPreprint
Abdollahpouri, H., & Mansoury, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15772 · Cited by 61 · cited 2× · see citing works
“This phenomenon has been empirically verified by Abdollahpouri [24].”
BackgroundResultSamWalker: Social Recommendation with Informative Sampling Strategy
Chen, J., Wang, C., Zhou, S., Shi, Q., Feng, Y., & Chen, C. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313582 · Cited by 59 · see citing works
Self-citationChen, J., Feng, Y., Ester, M., Zhou, S., Chen, C., & Wang, C. (2018) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3269206.3271742 · Cited by 38 · cited 1× · see citing works
“[37], [38], [39]; Exposure bias, which happens in implicit feedback data as users are only exposed to a part of specific items [24], [36], [40], [41]; Position bias, which happens as users tend to interact with items in higher position of the recommendation list [36], [42]; Unfairness [43], [44], which”
BackgroundSelf-citationSocial Recommendation with Missing Not at Random Data
Chen, J., Wang, C., Ester, M., et al. (2018) · Proceedings · doi:10.1109/icdm.2018.00018 · Cited by 36 · cited 1× · see citing works
“Besides popularity bias, recent works have studied other types of biases in recommendation including: Selection bias, which happens as users are free to choose which items to rate, so that the observed ratings are not a representative sample of all ratings [36], [37], [38], [39]; Exposure bias, which happens in implic…”
BackgroundSelf-citationSerendipitous Personalized Ranking for Top-N Recommendation
Lu, Q., Chen, T., Zhang, W., Yang, D., & Yu, Y. H. (2012) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat.2012.135 · Cited by 36 · cited 1× · see citing works
“It would decrease serendipity [25], [26], [27] and fairness [13], [28], [29], [30] of recommendation results, hurting user experience and causing customer churn.”
BackgroundWu, L., He, X., Wang, X., Zhang, K., & Wang, M. (2021) · arXiv (Cornell University) · Cited by 26 · see citing works
Self-citationThe Impact of Popularity Bias on Fairness and Calibration in RecommendationPreprint
Abdollahpouri, H., Mansoury, M., Burke, R., & Mobasher, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05755 · Cited by 18 · cited 1× · see citing works
“It would decrease serendipity [25], [26], [27] and fairness [13], [28], [29], [30] of recommendation results, hurting user experience and causing customer churn.”
Background
Flagged first, then by citations
1 of the 48 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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 · see citing works
“Zheng et al. [217] propose to model personalized conformity effect as users have different sensitivities to the public opinions; while Zhao et al. [215] model time-aware conformity effect by considering item dynamic popularity.”
BackgroundSelf-citationA 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 179 · see citing works
“[148], for example, found that “the observation data usually exhibits”
BackgroundA survey of sequential recommendation systems: Techniques, evaluation, and future directions
Boka, T. F., Niu, Z., & Neupane, R. B. (2024) · Information Systems · doi:10.1016/j.is.2024.102427 · Cited by 98 · see citing works
Distributionally Robust Graph-based Recommendation System
Wang, B., Chen, J., Li, C., Zhou, S., Shi, Q., Gao, Y., Feng, Y., Chen, C., & Wang, C. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645598 · Cited by 45 · see citing works
Self-citationLalor, J. P., Abbasi, A., Oketch, K., Yang, Y., & Forsgren, N. (2024) · ACM Transactions on Information Systems · doi:10.1145/3641276 · Cited by 36 · see citing works
“For instance, the recommender systems literature has explored the implications of user behavior biases on future model recommendations/performance for many years—namely, the effect of data/sampling biases pertaining to selection, position, exposure, and popularity [34, 64, 75, 108].”
BackgroundAdaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering
Zhou, H., Chen, H., Dong, J., Zha, D., Zhou, C. E., & Huang, X. X. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591635 · Cited by 33 · see citing works
“Causal graphs can also be applied to understand and mitigate bias [32, 36, 48, 51, 53].”
BackgroundLong tail service recommendation based on cross-view and contrastive learning
Yu, D., Yu, T., Wang, D., et al. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121957 · Cited by 32 · see citing works
Cui, Y., Liu, F., Wang, P., Wang, B., Tang, H., Wan, Y., Wang, J., & Chen, J. (2024) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3640457.3688118 · Cited by 31 · see citing works
Self-citationHow graph convolutions amplify popularity bias for recommendation?
Chen, J., Wu, J., Chen, J., Xin, X., Li, Y., & He, X. (2023) · Frontiers of Computer Science · doi:10.1007/s11704-023-2655-2 · Cited by 30 · see citing works
“Directly eliminating all the bias is not reasonable in recommendation, which conforms with the finding of [21].”
Self-citationA review of causality-based fairness machine learning
Su, C., Yu, G., Wang, J., Yan, Z., & Cui, L. (2022) · Intelligence & Robotics · doi:10.20517/ir.2022.17 · Cited by 24 · see citing works
Collaborative Tag-Aware Graph Neural Network for Long-Tail Service Recommendation
Zhang, Z., Zhang, Y., Dong, M., et al. (2024) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2024.3349853 · Cited by 24 · see citing works
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 · see citing works
“This implies that ¯ v tends to gravitate towards the origin.”
Self-citationCDR: Conservative Doubly Robust Learning for Debiased Recommendation
Song, Z., Chen, J., Zhou, S., Shi, Q., Feng, Y., Chen, C., & Wang, C. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614805 · Cited by 23 · see citing works
“There are various sources of bias found in RS data, such as selection bias [7, 34, 35], exposure bias [8, 9, 29], conformity bias [30, 43], position bias [22, 23] and popularity bias [1, 10, 48, 54].”
BackgroundSelf-citationInteract with the Explanations: Causal Debiased Explainable Recommendation System
Liu, X., Yu, T., Xie, K., Wu, J., & Li, S. (2024) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3616855.3635855 · Cited by 20 · see citing works
“[52]analyzebiasinrecommendationsystemsfromaconformityef-fectperspectiveandproposecorrespondingcausalinterventions onpre-defineditempopularitytoenablefairrecommendations.”
BackgroundCausal Distillation for Alleviating Performance Heterogeneity in Recommender Systems
Zhang, S., Jiang, Z., Yao, J., Feng, F., Kuang, K., Zhao, Z., Li, S., Yang, H., Chua, T., & Wu, F. Q. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3290545 · Cited by 18 · see citing works
“One representative line of research to alleviate these biases is to explicitly model or utilize these biases [30].”
BackgroundSelf-citationEnhancing Recommendation with Search Data in a Causal Learning Manner
Si, Z., Sun, Z., Zhang, X., Xu, J., Song, Y., Zang, X., & Wen, J.-R. (2023) · ACM Transactions on Information Systems · doi:10.1145/3582425 · Cited by 17 · see citing works
LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation
Wang, B., Liu, F., Zhang, C., Chen, J., Wu, Y., Zhou, S., Lou, X., Wang, J., Feng, Y., Chen, C., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3762182 · Cited by 29 · see citing works
Self-citationGeneral Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout
Zhang, A., Ma, W., Wei, P., Sheng, L., & Wang, X. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645667 · Cited by 15 · see citing works
Relation pruning and discriminative sampling over knowledge graph for long-tail recommendation
Zhang, Z., Wang, A., Zhang, Y., et al. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120871 · Cited by 15 · see citing works
Transfer learning for collaborative recommendation with biased and unbiased data
Lin, Z., Liu, D., Pan, W., et al. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103992 · Cited by 14 · see citing works
Simulating News Recommendation Ecosystems for Insights and Implications
Zhang, G., Li, D., Gu, H., Lu, T., Shang, L., & Gu, N. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3381329 · Cited by 15 · see citing works
“Regarding the relationship between popularity and quality, Zhao et al. [16] and Ciampaglia et al. [17] pointed out that under certain conditions, high quality leads to high popularity.”
BackgroundA general tail item representation enhancement framework for sequential recommendation
Cheng, M., Liu, Q., Zhang, W., Liu, Z., Zhao, H., & Chen, E. (2023) · Frontiers of Computer Science · doi:10.1007/s11704-023-3112-y · Cited by 13 · see citing works
Cai, M., Hou, M., Chen, L., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3664931 · Cited by 13 · see citing works
Path-Specific Causal Reasoning for Fairness-aware Cognitive Diagnosis
Zhang, D., Zhang, K., Wu, L., Tian, M., Hong, R., & Wang, M. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3672049 · Cited by 12 · see citing works
Debiased Recommendation with User Feature Balancing
Yang, M., Cai, G., Liu, F., Jin, J., Dong, Z., He, X., Hao, J., Shao, W., Wang, Y.-X., & Chen, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3580594 · Cited by 13 · see citing works
“Traditionally, there are two types of strategies to build debiased recommender models [5, 7, 8, 42, 51, 51].”
BackgroundSelf-citationToward Bias-Agnostic Recommender Systems: A Universal Generative Framework
Wang, Z., Zou, L., Li, C., Wang, S., Chen, X., Yin, D., & Liu, W. (2024) · ACM Transactions on Information Systems · doi:10.1145/3655617 · Cited by 11 · see citing works
“— TIDE [63]: A state-of-the-art causal inference method that only removes the harmful effect of popularity bias while leveraging its benign part to make recommendations.”
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 · see citing works
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 · see citing works
Cao, T., Xu, Q., Yang, Z., & Huang, Q. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3327411 · Cited by 9 · see citing works
“Furthermore, [26] and [27] propose methods to preserve useful information when causally intervening the item popularity bias.”
MethodTowards 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 · see citing works
Reproducing Popularity Bias in Recommendation: The Effect of Evaluation Strategies
Daniil, S., Cuper, M., Liem, C. C. S., van Ossenbruggen, J., & Laura Hollink, A. (2023) · ACM Transactions on Recommender Systems · doi:10.1145/3637066 · Cited by 9 · see citing works
“However, very popular items are often more likely to be already known, which renders the recommendation of mostly popular items to a user potentially not useful for the user and the system in general [1].”
BackgroundMultimodal recommender systems: A survey of representation, modeling, and optimization
Pan, Z., Pan, Z., Cai, F., & Chen, H. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103991 · Cited by 10 · see citing works
Multi-Cause Deconfounding for Recommender Systems with Latent Confounders
Zhi-rong, H., Hu, Y., Cheng, D., et al. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114345 · Cited by 8 · see citing works
Self-Adaptive Deep Asymmetric Network for Imbalanced Recommendation
Zhu, Y. Q., Geng, Y., Li, J., et al. (2023) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2023.3300740 · Cited by 8 · see citing works
“Zhao et al. proposed to leverage temporal information to distinguish benign and harmful popularity bias [41], and the final interventional inference is conducted in the recommendation for benefiting from the benign popularity bias.”
BackgroundCapturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation
Jing, J., Zhang, Y., Zhou, X., & Shen, Z. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3614801 · Cited by 9 · see citing works
“As a result, recent studies have been focusing on reducing popularity bias while maintaining the models’ performances [70, 71, 77].”
BackgroundWang, X., Rahmani, H. A., Liu, J., & Yılmaz, E. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.233 · Cited by 8 · see citing works
Modeling Dynamic Item Tendency Bias in Sequential Recommendation With Causal Intervention
Liao, Z., Wu, X., Shang, S., Wang, J., & Zhang, W. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3427719 · Cited by 8 · see citing works
Deconfounding User Preference in Recommendation Systems through Implicit and Explicit Feedback
Liang, Y., Yang, E., Guo, G., et al. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3673762 · Cited by 7 · see citing works
“However, historical interaction data may be confounded by various factors, resulting in biases, such as selection bias [15, 23], popularity bias [28, 29], and exposure bias [1, 26].”
Invariant debiasing learning for recommendation via biased imputation
Bai, T., Chen, W., Yang, C., et al. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.104028 · Cited by 7 · see citing works
Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
Liu, Y., Cao, Q., Shen, H., Wu, Y., Tao, S., & Cheng, X. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591947 · Cited by 8 · see citing works
Pan, Q., Li, S., Zheng, T., Ma, F., & Huang, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123227 · Cited by 7 · see citing works
Rankformer: A Graph Transformer for Recommendation based on Ranking Objective
Chen, S., Shen, H., Chen, J., et al. (2025) · doi:10.1145/3696410.3714547 · Cited by 5 · see citing works
Self-citationCoherence-guided Preference Disentanglement for Cross-domain Recommendations
Xiang, Z., Zhang, Y., Duan, L., Yin, H., & Tsang, I. W.-H. (2025) · ACM Transactions on Information Systems · doi:10.1145/3742855 · Cited by 6 · see citing works
“Therefore, CoPD achieve significant performance improvement compared with UniCDR in all CDR tasks.”
MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender
Wang, B., Liu, F., Chen, J., Lou, X., Zhang, C., Wang, J., Sun, Y., Feng, Y., Chen, C., & Wang, C. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730041 · Cited by 5 · see citing works
Self-citationTaming Recommendation Bias with Causal Intervention on Evolving Personal Popularity
Tan, S., Li, D., Jiang, R., et al. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737146 · Cited by 5 · see citing works
Causal inference for alleviating confounding bias in multi-criteria rating recommendation
Guo, Z., Song, P., Feng, C., Yao, K., & Liang, J. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104364 · Cited by 5 · see citing works
CaDRec: Contextualized and Debiased Recommender Model
Wang, X., Fukumoto, F., Cui, J., Suzuki, Y., Li, J., & Yu, D. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657799 · Cited by 5 · see citing works
Causal Inference and Recommendations
Zhu, Y., Ma, J., & Li, J. (2023) · Springer eBooks · doi:10.1007/978-3-031-35051-1_10 · Cited by 6 · see citing works
Learning-to-rank debias with popularity-weighted negative sampling and popularity regularization
Wang, C., Liu, A., & Qin, T. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127681 · Cited by 5 · see citing works
A neighborhood-based method for mining and fusing positive and negative false samples
Pan, Q., Zheng, T., Li, S., Zhang, Z., & Jiang, J. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111345 · Cited by 4 · see citing works
Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation
Hu, W., Sun, X., Liu, Q., Wu, L., & Wang, L. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3552658 · Cited by 5 · see citing works
“Bias in recommendation systems is a significant concern in current research [66], [67], [68], impacting the fairness and diversity of recommendations.”
Learning instrumental variable representation for debiasing in recommender systems
Zhi-rong, H., Zhang, S., Cheng, D., Li, J., Liu, L., Lu, G., & Zhang, G. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107977 · Cited by 8 · see citing works
Disentangled Variational Autoencoder for Social Recommendation
Zhang, Y., Huang, J., & Yang, J. Y. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11607-y · Cited by 4 · see citing works
“Liu et al. [28] and Zhao et al. [29] individually disentangled biased and unbiased factors for recommendations.”
Model-Agnostic Dual-Side Online Fairness Learning for Dynamic Recommendation
Tang, H., Wu, S., Cui, Z., et al. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3544510 · Cited by 4 · see citing works
“There has been a growing awareness that recommendation systems are subject to algorithmic fairness considerations along different dimensions, including stakeholder (e.g., producer and consumer), type of benefit, context, and even morality [8], [9], [10], [11], [12].”
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 · see citing works
“For instance, Zhao et al. [43] utilized the causal graph to analyze the effect of popularity bias in RSs. Chen et al. [44] proposed an unbiased recommendation framework for knowledge distillation.”
Counteracting Popularity Bias in Multimedia Web API Recommendation
Zhai, D., Yan, C., Zhong, W., Ding, S., Qi, L., & Zhou, X. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3517601 · Cited by 3 · see citing works
“However, unbiased data needs to be ar-tificially achieved, which leads to the high price of unbiased data [41].”
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 · see citing works
Mitigating influence of selection and conformity biases for user interest in recommender systems
Zheng, T., Xu, X., Li, S., et al. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.113810 · Cited by 3 · see citing works
TEXT CAN BE FAIR: Mitigating Popularity Bias with PLMs by Learning Relative Preference
Tang, Z., Huan, Z., Li, Z., et al. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679581 · Cited by 3 · see citing works
CSLP: Collaborative Solution to Long-Tail Problem and Popularity Bias in Sequential Recommendation
Huang, Y., Yang, Z., Hu, W., Xu, B., & Zhang, Z. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831683 · Cited by 3 · see citing works
“Recent researches [3]– [6], [22] report significant progress in mitigating popularity bias.”
Causal Inference for Multi-Criteria Rating Recommender Systems
Guo, Z., Song, P., Feng, C., Yao, K., & Liang, J. (2025) · ACM Transactions on Information Systems · doi:10.1145/3757737 · Cited by 3 · see citing works
“For RSs, causal inference can effectively alleviate the spurious association and bias problems (such as popularity bias [74], location bias [23], exposure bias [48], etc.) caused by confounding variables [11].”
Popularity Bias in Correlation Graph-based API Recommendation for Mashup Creation
Yan, C., Zhong, W., Zhai, D., Khan, A. A., Gong, W., Xu, Y., & Xin, B. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3654445 · Cited by 2 · see citing works
“Researchers are increasingly aware of the problem of popularity bias in recommender systems and have conducted many research works [ 1 , 7 , 14 , 20 , 24 , 42 , 43 ].”
Causal Disentanglement for Tackling Popularity Bias in Sequential Recommendation
Liu, A.-A., Zhao, Y., Wen, X., Chang, R., & Nie, W. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3575554 · Cited by 2 · see citing works
“Additionally, with the development of causal inference technology, it has been widely applied in recommendation systems [36].”
Neural recommendation by user-item-user and item-user-item relation modeling
Xu, Y.-Y., Xiao, H., Zhang, H., Wang, D.-D., Wang, K., & Min, F. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112104 · Cited by 2 · see citing works
Huang, B., Wang, Z., Yan, F., et al. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113928 · Cited by 2 · see citing works
Ding, H., Zhu, W., Hu, G., et al. (2025) · Systems · doi:10.3390/systems13111034 · Cited by 2 · see citing works
Graph-Structured Driven Dual Adaptation for Mitigating Popularity Bias
Cai, M., Chen, L., Wang, Y., Cheng, Z., Zhang, M., & Wang, M. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3638343 · Cited by 2 · see citing works
“However, popularity bias persists as a critical challenge, wherein highly-interacted items dominate recommendation lists, causing unpopular items to be increasingly marginalized [6], [7], [8].”
FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network
Liu, Z., & Luo, W. (2023) · Applied Sciences · doi:10.3390/app13137975 · Cited by 2 · see citing works
Addressing popularity discrepancy in collaborative filtering
Yu, C., Li, D., Gu, H., Zhang, P., Gu, N., & Lu, T. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02426-1 · Cited by 2 · see citing works
“TIDE [17] is a novel time-aware disentangled framework, enabling interventional inference to leverage beneficial popularity bias while mitigating harmful bias.”
InfluentialUnbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender Systems
Kang, S., Kweon, W., Lee, D., Lian, J., Xie, X., & Yu, H. (2024) · ACM Transactions on Recommender Systems · doi:10.1145/3649443 · Cited by 2 · see citing works
“There have been many attempts to tackle the popularity lift [ 32 , 33 , 52 , 63 ], including approaches such as invariant learning [ 62 ], specialized architectures [ 30 ], and popularity-robust loss functions [ 43 ].”
Learning Robust Sequential Recommenders through Confident Soft Labels
Wu, S., Xin, X., Ren, P., et al. (2024) · ACM Transactions on Information Systems · doi:10.1145/3700876 · Cited by 1 · see citing works
“For a deeper analysis, popularity bias is not always harmful for the recommender according to Zhao et al. [43], popular items sometimes do reflect the general user interests.”
Are Representation Disentanglement and Interpretability Linked in Recommendation Models?
Dervishaj, E., Ruotsalo, T., Maistro, M., & Lioma, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88717-8_4 · Cited by 1 · see citing works
TDIC: Time-Aware Disentanglement of Interest and Conformity in Mobile App Recommendations
Li, Q., Zhao, Y., & Ma, Y. (2025) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws67624.2025.00011 · Cited by 2 · see citing works
“To enhance recommendation fairness and accuracy, we must better capture the distinct roles of temporal conformity and item quality within popularity [10], [11], using causal models that adapt to time-evolving user behavior.”
Popularity debiasing of multi-behavior recommendation via causal inference and data augmentation
Bin, C., Chen, H., & Zhang, F. (2025) · Applied Intelligence · doi:10.1007/s10489-025-07053-5 · Cited by 1 · see citing works
“For instance, Zhao et al. [ 28] mitigate the direct impact of item popularity by tracing the consistency effect and applying causal interventions.”
Self-citationEvolution and impact of AI in knowledge sharing: insights from a bibliometric analysis
Trinh, N., Nguyen, M., & Ngo, D.-T. (2025) · Evolutionary Intelligence · doi:10.1007/s12065-025-01129-4 · Cited by 1 · see citing works
“They are affected by the same algorithmic biases common in commercial recommendation systems, including popularity bias and exposure bias [53–55].”
SAGERec: Sampling and Gating for Enhanced Long-Tail Item Recommendations
Alshabanah, A., Yang, Y., & Annavaram, M. (2026) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3773966.3778004 · Cited by 1 · see citing works
Fairness-Aware Group Recommendation with Spatio-Temporal Feature Fusion
Jia, J., Wang, F., & Liu, S. (2026) · Measurement · doi:10.1016/j.measurement.2026.121344 · Cited by 1 · see citing works
PD-SRS: Personalized Diversity for a Fair Session-Based Recommendation System
Kermany, N. R., Pizzato, L. A. S., Yang, J. Y., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20984-0_23 · Cited by 1 · see citing works
Calibrating Popularity Bias Based on Quality for Recommendation Fairness
Guo, Z., Zhu, Y., Wang, Z., & Jing, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46677-9_26 · Cited by 1 · see citing works
Alleviating Confounding Effects with Contrastive Learning in Recommendation
You, D., & Lee, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56060-6_30 · Cited by 1 · see citing works
“To name a few, [3] categorized common biases into seven types, with selection bias [12, 27], exposure bias [18, 20, 22, 48] , and popularity bias [37, 43, 44, 46] being the most discussed.”
BackgroundBiasRec: A General Bias-Aware Social Recommendation Model
Zhang, C., & Li, G. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5572-1_7 · Cited by 1 · see citing works
Kralr: knowledge-enhanced representation aggregation for long-tail recommendation
Zhang, Z., Zhang, Y., Li, W., et al. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06208-w · Cited by 1 · see citing works
“Thus, popularity bias is widespread in RSs (Chen et al., 2023a; Zhao et al., 2021).”
Mitigating Confounding and Selection Biases in Personalized Recommendation: A Causal Approach
Huang, W. Q., Zhou, J., Wu, X., & Dou, D. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386496 · Cited by 1 · see citing works
“Both of them have been studied but still under-explored in causal recommendation literature [24], [26].”
BackgroundModeling Variational Anchoring Effect for Recommender Systems
Xiao, Y., Zhang, Y., & Li, X. (2024) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai59869.2024.00170
Chen, G.-M., Qiang, W., Ouyang, Y., et al. (2024) · doi:10.1109/ijcnn60899.2024.10651019
“Popularity bias refers to a common issue in recommender systems where certain popular items receive a disproportionately high level of attention compared to others [9], which means the distribution of interactions over items is quite imbalanced and even long-tailed [10].”
BackgroundMethodWang, Z., Zou, H., Liu, J., Wu, J., Tian, P., He, Y., & Cui, P. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3706637
“— AdaptSel outperforms both TIDE and PDA by adapting to the test environment.”
InfluentialDisentangled Causal Embedding with Unbiased Knowledge Distillation for Recommendation
Liu, N., Meng, S., Liu, X., Li, Q., & Jiang, Y. Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0850-8_9
Contrastive Disentangled Variational Autoencoder for Collaborative Filtering
Yun, W.-S., Kang, S.-M., & Cho, Y.-S. (2025) · IEEE Access · doi:10.1109/access.2025.3576445
Enhancing collaborative translational metric learning with causal graph network
Wang, J., Xie, H., Qin, S. J., Tao, X., Wang, F. L., & Xu, X. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130933
Modeling Social Behavior in Collaborative Filtering
Zhang, Y., & Hara, T. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3730039
SM-RS 2.0: User-perceived Qualities of Single- and Multi-Objective Recommender Systems
Dokoupil, P., & Peška, L. (2025) · ACM Transactions on Recommender Systems · doi:10.1145/3754459
“This is in line with, e.g., Zhao et al. [67] who state that a portion of popularity bias simply reflects better item qualities, rather than users’ conformity patterns.”
Wu, J., Chen, G., Pang, C., Zhao, J., & See-To, E. W. K. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108028
Xu, H., Yang, R., & Geng, R. (2025) · INFORMS journal on computing · doi:10.1287/ijoc.2024.0682
KGRL: Knowledge graph-enhanced representation learning for long-tail recommendation
Zhang, Z., Zhang, Y., Wang, M., et al. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.115063
Accurate and Diverse Recommendations via Propensity-Weighted Linear Autoencoders
Onishi, K., Hayashi, K., & Kamigaito, H. (2025) · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP) · doi:10.1145/3767695.3769512
KGNS: Knowledge graph-driven neighbor selection for long-tail recommendations
Zhang, Z., Zhang, Y., & Ren, Y. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104561
Adaptive disentangled learning recommendation via similarity popularity
Ye, J., Wang, H., Deng, J., Zhang, Z., Wang, Y., Xu, Z., & Khushvakhtzoda, K. K. (2025) · Applied Intelligence · doi:10.1007/s10489-025-07050-8
“In recent years, causal theory has been widely applied in the recommendation systems, which improves the performances of various recommendation tasks [30, 31].”
The Unfairness of Multifactorial Bias in Recommendation
Mansoury, M., Huang, J., Pechenizkiy, M., van Hoof, H., & de Rijke, M. (2026) · ACM Transactions on Information Systems · doi:10.1145/3841477
“Notably, Zhao et al. [92] show that conformity is also a key driver of popularity bias, as users tend to interact with already popular items regardless of their actual preferences, further inflating the popularity of these items.”
- Authors
- Zhao, Z., Chen, J., Zhou, S., He, X., Cao, X., Zhang, F., & Wu, W.
- Affiliations
- University of Science and Technology of China (CN)
- Zhejiang University (CN)
- Meituan (China) (CN)
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2023-10-01
- Volume
- 35
- Issue
- 10
- Pages
- 9920-9931
- ISSN
- 1041-4347 (ISSN-L)
- 1558-2191
- 2326-3865
- OpenAlex
- W4312551924
- Semantic Scholar
- 3d361ead1582071a5ed26cff391504746a1210a6
- Cited by
- 117
- References
- 48
- Citations by year
- 2026: 21 · 2025: 36 · 2024: 35 · 2023: 21 · 2022: 4
- FWCI
- 23.75
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Knowledge and Data Engineering (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Key Research and Development Program of China (2021ZD0111802)
- National Natural Science Foundation of China (62102382, 61972372, U21B2026)
- Meituan Inc.
- CCCD Key Lab of Ministry of Culture and Tourism
- Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study (SN-ZJU-SIAS-001)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Social Sciences › Decision Sciences › Management Science and Operations Research › Advanced Bandit Algorithms Research
- Keywords
- popularity bias, recommender systems, item quality, temporal modeling, disentangled representations, click data
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.
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