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Permanent link (DOI): https://doi.org/10.7939/R31G0J19D

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Using Survival Prediction Techniques to Learn Consumer-Specific Reservation Price Distributions Open Access

Descriptions

Other title
Subject/Keyword
e-commerce
reservation price
survival analysis
profit maximization
censored regression
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Jin, Ping
Supervisor and department
Greiner, Russell (Computing Science)
Examining committee member and department
Schuurmans, Dale (Computing Science)
Greiner, Russell (Computing Science)
Bowling, Michael (Computing Science)
Department
Department of Computing Science
Specialization
Statistical Machine Learning
Date accepted
2015-01-26T11:09:28Z
Graduation date
2015-06
Degree
Master of Science
Degree level
Master's
Abstract
A consumer's "reservation price" (RP) is the highest price that s/he is willing to pay for one unit of a specified product or service. It is an essential concept in many applications, e.g., personalized pricing, auction and negotiation. While consumers will not volunteer their RPs, we may be able to predict these values, based on each consumer's specific information, using a model learned from earlier consumer transactions. This thesis proposes a novel framework of learning RP distributions that involves a model of formulating the relationship between consumers' RPs and their purchasing decisions, and a data collection method. Within this framework, we show a way to estimate the consumer-specific RP distribution using techniques from the survival prediction --- here viewing the consumers' purchasing choices as the censored observations. To validate our new framework of RP, we run experiments on realistic data, with four survival methods. These models performed very well (under three different criteria) on the task of estimating consumer-specific RP distributions, which shows that our RP framework can be effective. As we found that the multi-task logistic regression model (MTLR) dominated the other models under all three evaluation criteria, we explored ways to extend it, leading to extensions that are more general and more flexible. Moreover, we prove that it is the general regularizer, instead of the smoothness regularizer, that results in a smooth predicted distribution; this leads further simplification of the MTLR model.
Language
English
DOI
doi:10.7939/R31G0J19D
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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