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Massive Choice Data

Massive Choice Data. 7 th Triennial Choice Symposium Wharton Business School June 13 -17, 2007. Impetus for “Massive Data”. Technological advances (Internet, RFID) Computing advances Methodological advances Detailed data Large sample, N Many variables, p Long time-series, T

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Massive Choice Data

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  1. Massive Choice Data 7th Triennial Choice Symposium Wharton Business School June 13 -17, 2007

  2. Impetus for “Massive Data” • Technological advances (Internet, RFID) • Computing advances • Methodological advances • Detailed data • Large sample, N • Many variables, p • Long time-series, T • Several products and SKUs, K

  3. Goals • Understand current state of play • Identify issues of interest • Review advances in models, methods, computation, ideas • Discuss prospects for further research • Any other goals that we – as a group – deem relevant

  4. Outcome • Synthesis of our deliberations to be published as a review paper in the Marketing Letters

  5. Lynd Bacon President, LBA Associates www.lba.com lbacon@lba.com People

  6. Anand Bodapati UCLA anand.bodapati@anderson.ucla.edu

  7. Wagner Kamakura Duke University kamakura@duke.edu

  8. Jeffrey Kreulen IBM Research kreulen@almaden.ibm.com

  9. Peter Lenk University of Michigan plenk@umich.edu

  10. David Madigan Rutgers University dmadigan@rutgers.edu

  11. Alan Montgomery Carnegie Mellon University alm3@andrew.cmu.edu

  12. Prasad Naik University of California Davis panaik@ucdavis.edu

  13. Michel Wedel University of Maryland mwedel@rhsmith.umd.edu

  14. Issues: Day 1 • Session 1 (Alan) • Computational Challenges for Real-Time Marketing with Large Datasets • Session 2 (Lynd) • Understanding Choices and Preferences with Massive Complex Online Data • Session 3 (Wagner) • Some rambling comments on “High-Dimensional Data Analysis”

  15. Issues: Day 2 • Session 4 (Jeffrey) • Leveraging Structured and Unstructured Information Analytics to Create Business • Session 5 (David) • Statistical Modeling: Bigger and Bigger

  16. Issues: Day 3 • Session 6 (Anand) • Issues in the Modeling of Behavior in Online Social Networks • Session 7 (Michel) • State of the Art in Recommendation Systems • Session 8 (Peter) • Approximate Bayes Methods for Massive Data in Conditionally Conjugate Hierarchical Bayes Models • Session 9 (Prasad) • Review of Inverse Regression Methods for Dimension Reduction

  17. Issues: Day 4 (Sunday) • Plenary Session 1 • Plenary Session 2 • Noon: Adjourn

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