Modern probabilistic models for human choices and rankings
Abstract/Contents
- Abstract
- As datasets capturing human choices and rankings grow in richness and scale, particularly in online domains, there is an increasing need for more flexible models of ranking and choice. I introduce the Pairwise Choice Markov Chain (PCMC) model of discrete choice, which escapes traditional choice-theoretic axioms such as regularity and stochastic transitivity while remaining inferentially tractable. PCMC still satisfies the axiom of uniform expansion, a considerably weaker assumption than Luce's choice axiom, and PCMC retains the Multinomial Logit (MNL) as a special case. Using the notion of a choice representation to break a ranking into a sequence of choices, I develop a framework for turning models for discrete choice into models for ranking data, generalizing the idea used to build the seminal Plackett-Luce (PL) model from MNL. This framework allows us to leverage our progress in choice into novel models for ranking data and allows us to build flexible yet tractable models for complete and partial rankings from recent advances in choice models. I also discuss methods and subtleties of shrinkage estimation for choice data and demonstrate the efficacy of the models I propose in prediction tasks from a broad range of domains including transportation, competitions, voting, and sushi.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ragain, Stephen Wesley |
|---|---|
| Degree supervisor | Ugander, Johan |
| Thesis advisor | Ugander, Johan |
| Thesis advisor | Goel, Ashish |
| Thesis advisor | Johari, Ramesh, 1976- |
| Degree committee member | Goel, Ashish |
| Degree committee member | Johari, Ramesh, 1976- |
| Associated with | Stanford University, Department of Management Science and Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Stephen Ragain. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Stephen Wesley Ragain
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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