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Machine Learning for Consumers and Markets

Published: 14 August 2021 Publication History

Abstract

Consumers leave digital footprints through large volumes of heterogeneous data which is a wealth of commercial value for firms, waiting to be mined. While there are initial success stories, this area is still under-explored. Further research and communication between the ML community and business community are needed to better align the objectives and create more successful applications. While machine learning is equipped to handle a variety of raw data for predictive tasks, without the theoretical insights from economics and consumer behavior to guide ML models, extracting generalizable insights with clear managerial implications and formulating impactful policies remain elusive. This workshop aims to promote further communication between these disciplines to foster synergistic development of impactful research that could benefit one another.

References

[1]
Amit Dhurandhar, Vijay Iyengar, Ronny Luss, and Karthikeyan Shanmugam. Tip: Typifying the interpretability of procedures. arXiv preprint arXiv:1706.02952, 2017.
[2]
Finale Doshi-Velez and Been Kim. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608, 2017.
[3]
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. A survey of methods for explaining black box models. ACM Computing Surveys (CSUR), 51(5):93, 2018.
[4]
Himabindu Lakkaraju and Osbert Bastani. " how do i fool you?": Manipulating user trust via misleading black box explanations. arXiv preprint arXiv:1911.06473, 2019.
[5]
Zachary C Lipton. The mythos of model interpretability. arXiv preprint arXiv:1606.03490, 2016.
[6]
Liu Liu, Daria Dzyabura, and Natalie Mizik. Visual listening in: Extracting brand image portrayed on social media. Marketing Science, 39(4):669--686, 2020.
[7]
Joy Lu, Dokyun Lee, Taewan Kim, and David Danks. Good explanation for algorithmic transparency. 2020.
[8]
Tim Miller. Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 2018.
[9]
Oded Netzer, Ronen Feldman, Jacob Goldenberg, and Moshe Fresko. Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3):521--543, 2012.
[10]
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Vaughan, and Hanna Wallach. Manipulating and measuring model interpretability. arXiv preprint arXiv:1802.07810, 2018.
[11]
Cynthia Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5):206, 2019.
[12]
Artem Timoshenko and John R Hauser. Identifying customer needs from usergenerated content. Marketing Science, 38(1):1--20, 2019.

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  1. Machine Learning for Consumers and Markets

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    cover image ACM Conferences
    KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
    August 2021
    4259 pages
    ISBN:9781450383325
    DOI:10.1145/3447548
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 14 August 2021

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    Author Tags

    1. business intelligence
    2. consumers and markets
    3. machine learning

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