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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

lbacon@lba.com

People


Massive choice data

Anand Bodapati

UCLA

anand.bodapati@anderson.ucla.edu


Massive choice data

Wagner Kamakura

Duke University

kamakura@duke.edu


Massive choice data

Jeffrey Kreulen

IBM Research

kreulen@almaden.ibm.com


Massive choice data

Peter Lenk

University of Michigan

plenk@umich.edu


Massive choice data

David Madigan

Rutgers University

dmadigan@rutgers.edu


Massive choice data

Alan Montgomery

Carnegie Mellon University

alm3@andrew.cmu.edu


Massive choice data

Prasad Naik

University of California Davis

panaik@ucdavis.edu


Massive choice data

Michel Wedel

University of Maryland

mwedel@rhsmith.umd.edu


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