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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

Massive Choice Data

7th Triennial Choice Symposium

Wharton Business School

June 13 -17, 2007

impetus for massive data
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
goals
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
outcome
Outcome
  • Synthesis of our deliberations to be published as a review paper in the Marketing Letters
people
Lynd Bacon

President, LBA Associates

www.lba.com

[email protected]

People
slide9
Peter Lenk

University of Michigan

[email protected]

slide11
Alan Montgomery

Carnegie Mellon University

[email protected]

slide12
Prasad Naik

University of California Davis

[email protected]

slide13
Michel Wedel

University of Maryland

[email protected]

issues day 1
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”
issues day 2
Issues: Day 2
  • Session 4 (Jeffrey)
    • Leveraging Structured and Unstructured Information Analytics to Create Business
  • Session 5 (David)
    • Statistical Modeling: Bigger and Bigger
issues day 3
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
issues day 4 sunday
Issues: Day 4 (Sunday)
  • Plenary Session 1
  • Plenary Session 2
  • Noon: Adjourn
ad