國立虎尾科技大學 |

Learning Preferences from Choices and Rankings.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Learning Preferences from Choices and Rankings./
作者:	Seshadri, Arjun.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	169 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: A.
Contained By:	Dissertations Abstracts International83-03A.
標題:	Information science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28483310
ISBN:	9798505571637

Learning Preferences from Choices and Rankings.
Seshadri, Arjun.

Learning Preferences from Choices and Rankings. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 169 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: A.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

A large and growing experimental literature has shown that individual choices and judgements can be affected by irrelevant aspects of the context in which they are made. Despite these findings, much of the existing modeling work in preference learning still relies on the simplifying assumption that choices come from the maximization of a stable utility function. In this dissertation, we discuss our progress in tractably modeling violations of utility-based reasoning in choices and rankings at scale. First, we describe the context dependent random utility model (CDM), our choice model that captures a broad class of context effects while remaining inferentially tractable. Second, we consider testing when violations of a popular notion of rationality, the Independence of Irrelevant Alternatives (IIA), exist in practice. Our work contributes effective methods for testing IIA and characterizes the fundamental statistical limitations of doing so. Third, we show how our advances in choice modeling can be leveraged to develop the contextual repeated selection (CRS) model of ranking, a model that brings a natural multimodality and richness to the rankings space along with strong statistical guarantees.

ISBN: 9798505571637Subjects--Topical Terms:

561178
Information science.

Learning Preferences from Choices and Rankings.
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520 $a A large and growing experimental literature has shown that individual choices and judgements can be affected by irrelevant aspects of the context in which they are made. Despite these findings, much of the existing modeling work in preference learning still relies on the simplifying assumption that choices come from the maximization of a stable utility function. In this dissertation, we discuss our progress in tractably modeling violations of utility-based reasoning in choices and rankings at scale. First, we describe the context dependent random utility model (CDM), our choice model that captures a broad class of context effects while remaining inferentially tractable. Second, we consider testing when violations of a popular notion of rationality, the Independence of Irrelevant Alternatives (IIA), exist in practice. Our work contributes effective methods for testing IIA and characterizes the fundamental statistical limitations of doing so. Third, we show how our advances in choice modeling can be leveraged to develop the contextual repeated selection (CRS) model of ranking, a model that brings a natural multimodality and richness to the rankings space along with strong statistical guarantees.
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