1 / 17

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

bernad
Download Presentation

Massive Choice Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related