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Probabilistic Preference Databases and Their Applications in Voting with Uncertainty.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probabilistic Preference Databases and Their Applications in Voting with Uncertainty./
作者:	Ping, Haoyue.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	114 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29214055
ISBN:	9798802711231

Probabilistic Preference Databases and Their Applications in Voting with Uncertainty.
Ping, Haoyue.

Probabilistic Preference Databases and Their Applications in Voting with Uncertainty. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 114 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--New York University Tandon School of Engineering, 2022.

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

Preferences reflect people's choices among alternatives in various domains such as social choice, e-commerce, and information retrieval. With the help of modern data management, preference data has been generated and collected on a large scale. But these preferences are often incomplete or implicit (thus are inferred with uncertainty) in real-world applications, which motivates the study of data management techniques that accommodate uncertain preferences.This thesis will present an in-database approach to incorporate uncertain preferences using statistical ranking models, particularly the Repeated Insertion Model (RIM) and its special case, the Mallows model. Given this preference database, a data analyst can query preferences about specified items (e.g., is the movie Casablanca preferred to the movie Coco?), or items satisfying specified properties (e.g., is a romance movie preferred to a comedy movie?). After reducing the evaluation of these queries to an inference problem over RIM, I will present both exact and approximate solvers for this inference problem, and demonstrate their effectiveness empirically with extensive experiments.Social choice is a prominent domain for preference-related research. It is concerned with the aggregation of individual preferences for collective decision-making, such as voting in elections. Similar to the aforementioned challenge of probabilistic preferences, classical voting rules are often designed for complete rankings, and thus are inapplicable to incomplete or uncertain voter preferences. In this thesis, I will focus on the positional scoring rules, and propose a novel winner interpretation by rewarding candidates according to their expected performance. Then I will establish the theoretical hardness of this winner definition, and develop solvers to identify winners e"ciently in practice for a variety of voting profiles, e.g., a voting profile consisting of RIM instances.

ISBN: 9798802711231Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Computational social choice

Probabilistic Preference Databases and Their Applications in Voting with Uncertainty.
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