IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v140y2026ics0305048325002014.html

Towards popularity-aware recommendation: A multi-behavior enhanced framework with orthogonality constraint

Author

Listed:
  • Han, Yishan
  • Xu, Biao
  • Wang, Yao
  • Gao, Shanxing

Abstract

Top-K recommendation involves inferring latent user preferences and generating personalized recommendations accordingly, which is now ubiquitous in various decision systems. Nonetheless, recommender systems usually suffer from severe popularity bias, leading to the over-recommendation of popular items. Such a bias deviates from the central aim of reflecting user preference faithfully, compromising both customer satisfaction and retailer profits. Despite the prevalence, existing methods tackling popularity bias still have limitations due to the considerable accuracy-debias tradeoff and the sensitivity to extensive parameter selection, further exacerbated by the extreme sparsity in positive user-item interactions.

Suggested Citation

  • Han, Yishan & Xu, Biao & Wang, Yao & Gao, Shanxing, 2026. "Towards popularity-aware recommendation: A multi-behavior enhanced framework with orthogonality constraint," Omega, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:jomega:v:140:y:2026:i:c:s0305048325002014
    DOI: 10.1016/j.omega.2025.103475
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048325002014
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2025.103475?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Leon Yang Chu & Hamid Nazerzadeh & Heng Zhang, 2020. "Position Ranking and Auctions for Online Marketplaces," Management Science, INFORMS, vol. 66(8), pages 3617-3634, August.
    2. Gediminas Adomavicius & Jingjing Zhang, 2016. "Classification, Ranking, and Top-K Stability of Recommendation Algorithms," INFORMS Journal on Computing, INFORMS, vol. 28(1), pages 129-147, February.
    3. Vivek F. Farias & Andrew A. L, 2019. "Learning Preferences with Side Information," Management Science, INFORMS, vol. 65(7), pages 3131-3149, July.
    4. Marios Kokkodis & Panagiotis G. Ipeirotis, 2023. "The Good, the Bad, and the Unhirable: Recommending Job Applicants in Online Labor Markets," Management Science, INFORMS, vol. 69(11), pages 6969-6987, November.
    5. Guo, Mengzhuo & Liao, Xiuwu & Liu, Jiapeng & Zhang, Qingpeng, 2020. "Consumer preference analysis: A data-driven multiple criteria approach integrating online information," Omega, Elsevier, vol. 96(C).
    6. Gediminas Adomavicius & Jesse C. Bockstedt & Shawn P. Curley & Jingjing Zhang, 2013. "Do Recommender Systems Manipulate Consumer Preferences? A Study of Anchoring Effects," Information Systems Research, INFORMS, vol. 24(4), pages 956-975, December.
    7. Ren, Peijia & Liu, Xiaodan & Zhang, Wei-Guo, 2024. "Consumer preference analysis: Diverse preference learning with online ratings," Omega, Elsevier, vol. 125(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu, Lu & Pan, Yuchen & Wu, Desheng & Olson, David L., 2025. "A novel O2O service recommendation method based on dynamic preference similarity," Omega, Elsevier, vol. 133(C).
    2. Yuyan Wang & Long Tao & Xian Xing Zhang, 2025. "Recommending for a Multi-Sided Marketplace: A Multi-Objective Hierarchical Approach," Marketing Science, INFORMS, vol. 44(1), pages 1-29, January.
    3. Park, YoungSoo & Sim, Jeongeun & Kim, Bosung, 2022. "Online retail operations with “Try-Before-You-Buy”," European Journal of Operational Research, Elsevier, vol. 299(3), pages 987-1002.
    4. Mehmet Eren Ahsen & Mehmet Ulvi Saygi Ayvaci & Srinivasan Raghunathan, 2019. "When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis," Service Science, INFORMS, vol. 30(1), pages 97-116, March.
    5. Sariyer, Gorkem & Mangla, Sachin Kumar & Sozen, Mert Erkan & Li, Guo & Kazancoglu, Yigit, 2024. "Leveraging explainable artificial intelligence in understanding public transportation usage rates for sustainable development," Omega, Elsevier, vol. 127(C).
    6. Tino Werner, 2022. "Elicitability of Instance and Object Ranking," Decision Analysis, INFORMS, vol. 19(2), pages 123-140, June.
    7. Vibhanshu Abhishek & Kinshuk Jerath & Siddhartha Sharma, 2025. "The Impact of “Retail Media” on Online Marketplaces: Insights from a Field Experiment," Information Systems Research, INFORMS, vol. 36(1), pages 456-473, March.
    8. Wu, Xingli & Liao, Huchang, 2021. "Modeling personalized cognition of customers in online shopping," Omega, Elsevier, vol. 104(C).
    9. Tino Werner, 2023. "Quantitative robustness of instance ranking problems," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 335-368, April.
    10. Constant Berkhout & Abhi Bhattacharya & Carlos Bauer & Ross W. Johnson, 2024. "Revisiting the construct of data-driven decision making: antecedents, scope, and boundaries," SN Business & Economics, Springer, vol. 4(10), pages 1-23, October.
    11. Ruohan Zhan & Zhimei Ren & Susan Athey & Zhengyuan Zhou, 2024. "Policy Learning with Adaptively Collected Data," Management Science, INFORMS, vol. 70(8), pages 5270-5297, August.
    12. Rudolph T. Bedeley & Hui Hao & Torupallab Ghoshal, 2025. "Cognitive Biases in Online Opinion Platforms: A Review and Mapping," SAGE Open, , vol. 15(1), pages 21582440251, January.
    13. Nasim Mousavi & Panagiotis Adamopoulos & Jesse Bockstedt, 2023. "The Decoy Effect and Recommendation Systems," Information Systems Research, INFORMS, vol. 34(4), pages 1533-1553, December.
    14. Yuyang Tan & Hao Gong & Chunxiang Guo, 2025. "Bi-Objective Optimization of Product Selection and Ranking Considering Sequential Search," SAGE Open, , vol. 15(3), pages 21582440251, August.
    15. Sungsik Park & Woochoel Shin & Jinhong Xie, 2021. "The Fateful First Consumer Review," Marketing Science, INFORMS, vol. 40(3), pages 481-507, May.
    16. Qihua Liu & Xiaoyu Zhang & Liyi Zhang & Yang Zhao, 2019. "The interaction effects of information cascades, word of mouth and recommendation systems on online reading behavior: an empirical investigation," Electronic Commerce Research, Springer, vol. 19(3), pages 521-547, September.
    17. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl & Joshua Gawlitza, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Information Systems Research, INFORMS, vol. 32(3), pages 713-735, September.
    18. Ye, Xu & Wang, Yu & Shafiee, Sara, 2024. "An iterative consumer-centric and technology-driven product innovation strategy based on selective and dynamic consumer attention," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    19. Hamsa Bastani, 2021. "Predicting with Proxies: Transfer Learning in High Dimension," Management Science, INFORMS, vol. 67(5), pages 2964-2984, May.
    20. Kevin Bauer & Andrej Gill, 2024. "Mirror, Mirror on the Wall: Algorithmic Assessments, Transparency, and Self-Fulfilling Prophecies," Information Systems Research, INFORMS, vol. 35(1), pages 226-248, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:140:y:2026:i:c:s0305048325002014. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.