Massive choice data
Download
1 / 17

Massive Choice Data - PowerPoint PPT Presentation


  • 105 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Massive Choice Data' - alaric


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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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


Anand Bodapati

UCLA

[email protected]


Wagner Kamakura

Duke University

[email protected]


Jeffrey Kreulen

IBM Research

[email protected]


Peter Lenk

University of Michigan

[email protected]


David Madigan

Rutgers University

[email protected]


Alan Montgomery

Carnegie Mellon University

[email protected]


Prasad Naik

University of California Davis

[email protected]


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