Sequential event prediction
Name
Rudin_Sequential event.pdf
Size
2.08 MB
Format
Adobe PDF
Checksum (MD5)
6be07a102be6c3971c2c0aa83de7850c
Author(s)
Letham, Benjamin
Rudin, Cynthia
Madigan, David
Date Issued
June 2013
Journal
Machine Learning
Publisher
Springer Science+Business Media
Citation
Letham, Benjamin, Cynthia Rudin, and David Madigan. “Sequential Event Prediction.” Mach Learn 93, no. 2–3 (November 2013): 357–380.
Version
Author's final manuscript
Abstract
In sequential event prediction, we are given a “sequence database” of past event sequences to learn from, and we aim to predict the next event within a current event sequence. We focus on applications where the set of the past events has predictive power and not the specific order of those past events. Such applications arise in recommender systems, equipment maintenance, medical informatics, and in other domains. Our formalization of sequential event prediction draws on ideas from supervised ranking. We show how specific choices within this approach lead to different sequential event prediction problems and algorithms. In recommender system applications, the observed sequence of events depends on user choices, which may be influenced by the recommendations, which are themselves tailored to the user’s choices. This leads to sequential event prediction algorithms involving a non-convex optimization problem. We apply our approach to an online grocery store recommender system, email recipient recommendation, and a novel application in the health event prediction domain.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10994-013-5356-5
