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Bamshad Mobasher

Active 1993–2026

257
Papers
18,573
Citations
65
h-index
157
i10-index

Citations

Citations per year for Bamshad Mobasher1996: 1 citations1997: 14 citations1998: 28 citations1999: 71 citations2000: 110 citations2001: 154 citations2002: 278 citations2003: 379 citations2004: 457 citations2005: 426 citations2006: 437 citations2007: 465 citations2008: 360 citations2009: 506 citations2010: 522 citations2011: 590 citations2012: 588 citations2013: 560 citations2014: 610 citations2015: 678 citations2016: 585 citations2017: 589 citations2018: 655 citations2019: 587 citations2020: 536 citations2021: 623 citations2022: 415 citations2023: 430 citations2024: 405 citations2025: 306 citations2026: 76 citations

Citation sources

Countries

World map of the countries and regions citing this authorUnited States: 1,851 citing papers, 18% of this breakdownChina: 1,421 citing papers, 13.8% of this breakdownItaly: 499 citing papers, 4.8% of this breakdownUnited Kingdom: 420 citing papers, 4.1% of this breakdownGermany: 413 citing papers, 4% of this breakdownIndia: 391 citing papers, 3.8% of this breakdownAustralia: 376 citing papers, 3.7% of this breakdownSpain: 359 citing papers, 3.5% of this breakdownCanada: 345 citing papers, 3.3% of this breakdownFrance: 326 citing papers, 3.2% of this breakdownTaiwan: 256 citing papers, 2.5% of this breakdownSouth Korea: 249 citing papers, 2.4% of this breakdown
0%18%Other 32.9%

Institutions

Fields

  • Computer Science83.3%
  • Social Sciences4.4%
  • Decision Sciences3.1%
  • Physics and Astronomy2.5%
  • Business, Management and Accounting2.5%
  • Engineering1.3%
  • Other2.9%

Topics

  • Recommender Systems and Techniques17.6%
  • Web Data Mining and Analysis4.9%
  • Data Mining Algorithms and Applications4.2%
  • Data Management and Algorithms3.7%
  • Advanced Bandit Algorithms Research2.8%
  • Caching and Content Delivery2.6%
  • Other64.2%

Coauthors

All papers

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  1. Controlling Popularity Bias in Learning-to-Rank Recommendation

    Authors: Himan Abdollahpouri, Robin Burke, Bamshad Mobasher - Eleventh ACM Conference on Recommender Systems, RecSys 2017 cited by 420

  2. User-centered Evaluation of Popularity Bias in Recommender Systems

    Authors: Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, Edward C. Malthouse - Conference on User Modeling, UMAP 2021 cited by 137

  3. A Graph-Based Approach for Mitigating Multi-Sided Exposure Bias in Recommender Systems

    Authors: Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke - ACM Transactions on Information Systems, ACM Trans. Inf. Syst. 2021 cited by 66

  4. Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness

    Authors: Bamshad Mobasher, Robin Burke, Runa Bhaumik, Chad Williams - ACM Transactions on Internet Technology, ACM Trans. Internet Techn. 2007 cited by 465

  5. Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking

    Authors: Himan Abdollahpouri, Robin Burke, Bamshad Mobasher - FLAIRS 2019 cited by 107

  6. The Unfairness of Popularity Bias in Recommendation

    Authors: Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher - RMSE@RecSys 2019 cited by 281

  7. Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation

    Authors: Masoud Mansoury, Himan Abdollahpouri, Bamshad Mobasher, Mykola Pechenizkiy, Robin Burke, Milad Sabouri - arXiv (Cornell University), CoRR 2021 cited by 27

  8. Feedback Loop and Bias Amplification in Recommender Systems

    Authors: Masoud Mansoury, Himan Abdollahpouri, Mykola Pechenizkiy, Bamshad Mobasher, Robin Burke - International Conference on Information & Knowledge Management, CIKM 2020 cited by 42

  9. Advances in Web Mining and Web Usage Analysis, 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006, Philadelphia, PA, USA, August 20, 2006, Revised Papers

    Authors: Olfa Nasraoui, Myra Spiliopoulou, Jaideep Srivastava, Bamshad Mobasher, Brij M. Masand - Lecture notes in computer science, WebKDD 2007 cited by 34

  10. Context-Aware Recommender Systems

    Authors: Gediminas Adomavicius, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin - AI Magazine, AI Mag. 2011 cited by 1,365

  11. Classification features for attack detection in collaborative recommender systems

    Authors: Robin D. Burke, Bamshad Mobasher, Chad Williams, Runa Bhaumik - SIGKDD international conference on Knowledge discovery and data mining 2006 cited by 251

  12. Context-aware music recommendation based on latenttopic sequential patterns

    Authors: Negar Hariri, Bamshad Mobasher, Robin D. Burke - sixth ACM conference on Recommender systems, RecSys 2012 cited by 314

  13. Personalized recommendation in social tagging systems using hierarchical clustering

    Authors: Andriy Shepitsen, Jonathan Gemmell, Bamshad Mobasher, Robin D. Burke - conference on Recommender systems, RecSys 2008 cited by 527

  14. Defending recommender systems: detection of profile injection attacks

    Authors: Chad Williams, Bamshad Mobasher, Robin D. Burke - Service Oriented Computing and Applications, Serv. Oriented Comput. Appl. 2007 cited by 157

  15. Calibration in Collaborative Filtering Recommender Systems: a User-Centered Analysis

    Authors: Kun Lin, Nasim Sonboli, Bamshad Mobasher, Robin Burke - Conference on Hypertext and Social Media, HT 2020 cited by 24

  16. Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models

    Authors: Mehdi Hosseinzadeh Aghdam, Negar Hariri, Bamshad Mobasher, Robin D. Burke - Conference on Recommender Systems, RecSys 2015 cited by 64

  17. RecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems

    Authors: Robin Burke, Bamshad Mobasher, Dietmar Jannach, Gediminas Adomavičius, Yehuda Koren, Verus Pronk - 2011 cited by 69

  18. Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation

    Authors: Farzad Eskandanian, Bamshad Mobasher - Conference on User Modeling, UMAP 2020 cited by 20

  19. Web search personalization with ontological user profiles

    Authors: Ahu Sieg, Bamshad Mobasher, Robin D. Burke - sixteenth ACM conference on Conference on information and knowledge management, CIKM 2007 cited by 320

  20. A Clustering Approach for Personalizing Diversity in Collaborative Recommender Systems

    Authors: Farzad Eskandanian, Bamshad Mobasher, Robin Burke - Conference on User Modeling, UMAP 2017 cited by 52

  21. Research directions in session-based and sequential recommendation

    Authors: Dietmar Jannach, Bamshad Mobasher, Shlomo Berkovsky - User Modeling and User-Adapted Interaction, User Model. User Adapt. Interact. 2020 cited by 41

  22. The Impact of Popularity Bias on Fairness and Calibration in Recommendation

    Authors: Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher - arXiv (Cornell University), CoRR 2019 cited by 23

  23. The Connection Between Popularity Bias, Calibration, and Fairness in Recommendation

    Authors: Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher - Fourteenth ACM Conference on Recommender Systems, RecSys 2020 cited by 19

  24. Does the User Have A Theory of the Recommender? A Grounded Theory Study

    Authors: Mohammed Muheeb Ghori, Arman Dehpanah, Jonathan Gemmell, Hamed Qahri-Saremi, Bamshad Mobasher - Adjunct Proceedings of the 30th ACM Conference on User Modeling, UMAP (Adjunct Publication) 2022 cited by 16