Massive Choice Data 7 th Triennial Choice Symposium
- Slides: 17
Massive Choice Data 7 th Triennial Choice Symposium Wharton Business School June 13 -17, 2007
Impetus for “Massive Data” l l 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 l l l 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 l Synthesis of our deliberations to be published as a review paper in the Marketing Letters
People l Lynd Bacon l l l President, LBA Associates www. lba. com lbacon@lba. com
l Anand Bodapati l l UCLA anand. bodapati@ander son. ucla. edu
l Wagner Kamakura l l Duke University kamakura@duke. edu
l Jeffrey Kreulen l l IBM Research kreulen@almaden. ibm. com
l Peter Lenk l l University of Michigan plenk@umich. edu
l David Madigan l l Rutgers University dmadigan@rutgers. edu
l Alan Montgomery l l Carnegie Mellon University alm 3@andrew. cmu. edu
l Prasad Naik l l University of California Davis panaik@ucdavis. edu
l Michel Wedel l l University of Maryland mwedel@rhsmith. umd. edu
Issues: Day 1 l Session 1 (Alan) – l Session 2 (Lynd) – l Computational Challenges for Real-Time Marketing with Large Datasets Understanding Choices and Preferences with Massive Complex Online Data Session 3 (Wagner) – Some rambling comments on “High-Dimensional Data Analysis”
Issues: Day 2 l Session 4 (Jeffrey) – l Leveraging Structured and Unstructured Information Analytics to Create Business Session 5 (David) – Statistical Modeling: Bigger and Bigger
Issues: Day 3 l Session 6 (Anand) – l Session 7 (Michel) – l State of the Art in Recommendation Systems Session 8 (Peter) – l Issues in the Modeling of Behavior in Online Social Networks 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) l Plenary Session 1 l Plenary Session 2 l Noon: Adjourn
Triennial customs broker fee
Bambonite
Cs246 stanford
Good choice or bad choice
There once was a woman from china
Massive igneous rocks
Seabed massive sulphides (sms)
Massive transformation purpose
Complications of blood transfusion
Suppose a cannon is propped against a massive tree
Mining massive datasets
Excessive use of ornamentation and massive proportions
Massive transfusion complication
Complications of blood transfusion
Massive hemothorax
Massive hemothorax
Massive wood chipper
Protostar
