IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v29y2017i3p503-522.html
   My bibliography  Save this article

Active Learning with Multiple Localized Regression Models

Author

Listed:
  • Meghana Deodhar

    (Google Inc., Mountain View, California 94043)

  • Joydeep Ghosh

    (Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712)

  • Maytal Saar-Tsechansky

    (Department of Information Risk and Operations Management, McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712)

  • Vineet Keshari

    (Google Inc., Mountain View, California 94043)

Abstract

Oftentimes businesses face the challenge of requiring costly information to improve the accuracy of prediction tasks. One notable example is obtaining informative customer feedback (e.g., customer-product ratings via costly incentives) to improve the effectiveness of recommender systems. In this paper, we develop a novel active learning approach, which aims to intelligently select informative training instances to be labeled so as to maximally improve the prediction accuracy of a real-valued prediction model. We focus on large, heterogeneous, and dyadic data, and on localized modeling techniques, which have been shown to model such data particularly well, as compared to a single, “global” model. Importantly, dyadic data with covariates is pervasive in contemporary big data applications such as large-scale recommender systems and search advertising. A key benefit from incorporating dyadic information is their simple, meaningful representation of heterogeneous data, in contrast to alternative local modeling techniques that typically produce complex and incomprehensible predictive patterns. We develop a computationally efficient active learning policy specifically tailored to exploit multiple local prediction models to identify informative acquisitions. Existing active learning policies are often computationally prohibitive for the setting we explore, and our policy makes the application of active learning computationally feasible for this setting. We present comprehensive empirical evaluations that demonstrate the benefits of our approach and explore its performance in real world, challenging domains.

Suggested Citation

  • Meghana Deodhar & Joydeep Ghosh & Maytal Saar-Tsechansky & Vineet Keshari, 2017. "Active Learning with Multiple Localized Regression Models," INFORMS Journal on Computing, INFORMS, vol. 29(3), pages 503-522, August.
  • Handle: RePEc:inm:orijoc:v:29:y:2017:i:3:p:503-522
    DOI: 10.1287/ijoc.2016.0732
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/ijoc.2016.0732
    Download Restriction: no

    File URL: https://libkey.io/10.1287/ijoc.2016.0732?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
    ---><---

    References listed on IDEAS

    as
    1. Maytal Saar-Tsechansky & Prem Melville & Foster Provost, 2009. "Active Feature-Value Acquisition," Management Science, INFORMS, vol. 55(4), pages 664-684, April.
    2. Zhiqiang Zheng & Balaji Padmanabhan, 2006. "Selectively Acquiring Customer Information: A New Data Acquisition Problem and an Active Learning-Based Solution," Management Science, INFORMS, vol. 52(5), pages 697-712, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bag, Sujoy & Tiwari, Manoj Kumar & Chan, Felix T.S., 2019. "Predicting the consumer's purchase intention of durable goods: An attribute-level analysis," Journal of Business Research, Elsevier, vol. 94(C), pages 408-419.
    2. Xuan Bi & Mochen Yang & Gediminas Adomavicius, 2024. "Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations," Information Systems Research, INFORMS, vol. 35(1), pages 339-362, March.
    3. Shao-Bo Lin & Shaojie Tang & Yao Wang & Di Wang, 2022. "Toward Efficient Ensemble Learning with Structure Constraints: Convergent Algorithms and Applications," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3096-3116, November.

    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. Kaiquan Xu & Stephen Shaoyi Liao & Raymond Y. K. Lau & J. Leon Zhao, 2014. "Effective Active Learning Strategies for the Use of Large-Margin Classifiers in Semantic Annotation: An Optimal Parameter Discovery Perspective," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 461-483, August.
    2. Jing Wang & Panagiotis G. Ipeirotis & Foster Provost, 2017. "Cost-Effective Quality Assurance in Crowd Labeling," Information Systems Research, INFORMS, vol. 28(1), pages 137-158, March.
    3. Zhepeng Li & Xiao Fang & Xue Bai & Olivia R. Liu Sheng, 2017. "Utility-Based Link Recommendation for Online Social Networks," Management Science, INFORMS, vol. 63(6), pages 1938-1952, June.
    4. Shantanu Gupta & Zachary C. Lipton & David Childers, 2021. "Efficient Online Estimation of Causal Effects by Deciding What to Observe," Papers 2108.09265, arXiv.org, revised Oct 2021.
    5. Zhiyuan Wang & Zhiqiang (Eric) Zheng & Wei Jiang & Shaojie Tang, 2021. "Blockchain‐Enabled Data Sharing in Supply Chains: Model, Operationalization, and Tutorial," Production and Operations Management, Production and Operations Management Society, vol. 30(7), pages 1965-1985, July.
    6. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    7. Maytal Saar-Tsechansky & Prem Melville & Foster Provost, 2009. "Active Feature-Value Acquisition," Management Science, INFORMS, vol. 55(4), pages 664-684, April.
    8. Xiaoping Liu & Xiao-Bai Li & Sumit Sarkar, 2023. "Cost-Restricted Feature Selection for Data Acquisition," Management Science, INFORMS, vol. 69(7), pages 3976-3992, July.
    9. Hung-Pin Kao & Kwei Tang, 2014. "Cost-Sensitive Decision Tree Induction with Label-Dependent Late Constraints," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 238-252, May.
    10. Yingfei Wang & Inbal Yahav & Balaji Padmanabhan, 2024. "Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19," Information Systems Research, INFORMS, vol. 35(1), pages 120-144, March.
    11. Wolfgang Ketter & Karsten Schroer & Konstantina Valogianni, 2023. "Information Systems Research for Smart Sustainable Mobility: A Framework and Call for Action," Information Systems Research, INFORMS, vol. 34(3), pages 1045-1065, September.
    12. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    13. Rajkumar Venkatesan & Alexander Bleier & Werner Reinartz & Nalini Ravishanker, 2019. "Improving customer profit predictions with customer mindset metrics through multiple overimputation," Journal of the Academy of Marketing Science, Springer, vol. 47(5), pages 771-794, September.
    14. Alain Bensoussan & Radha Mookerjee & Vijay Mookerjee & Wei T. Yue, 2009. "Maintaining Diagnostic Knowledge-Based Systems: A Control-Theoretic Approach," Management Science, INFORMS, vol. 55(2), pages 294-310, February.
    15. Nigel Melville & Michael McQuaid, 2012. "Research Note ---Generating Shareable Statistical Databases for Business Value: Multiple Imputation with Multimodal Perturbation," Information Systems Research, INFORMS, vol. 23(2), pages 559-574, June.
    16. Xuan Bi & Mochen Yang & Gediminas Adomavicius, 2024. "Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations," Information Systems Research, INFORMS, vol. 35(1), pages 339-362, March.
    17. Zhiqiang (Eric) Zheng & Peter Fader & Balaji Padmanabhan, 2012. "From Business Intelligence to Competitive Intelligence: Inferring Competitive Measures Using Augmented Site-Centric Data," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 698-720, September.
    18. Fan Zhou & Kunpeng Zhang & Bangying Wu & Yi Yang & Harry Jiannan Wang, 2021. "Unifying Online and Offline Preference for Social Link Prediction," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1400-1418, October.
    19. H-V Seow, 2010. "Question selection responding to information on customers from heterogeneous populations to select offers that maximize expected profit," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 443-454, March.

    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:inm:orijoc:v:29:y:2017:i:3:p:503-522. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.