國立虎尾科技大學 |

Bandits and Preference Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Bandits and Preference Learning./
作者:	Aniruddha, Bhargava.
面頁冊數:	1 online resource (168 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
Contained By:	Dissertation Abstracts International79-03B(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355524703

Bandits and Preference Learning.
Aniruddha, Bhargava.

Bandits and Preference Learning. - 1 online resource (168 pages)

Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.

Includes bibliographical references

The internet revolution has brought a large population access to a vast array of infor- mation since the mid 1990s. More recently, with the advent of smartphones, it has become an essential part of our everyday life. This has lead to, among many other developments, the personalization of the online experience with great benefits to all involved. Companies have particular interest in showing products and advertisements that match what particular users are looking for, and users desire getting personalized recommendations from internet for entertainment and consumer goods that suit them as individuals. In machine learning, this is popularly achieved using the theory of the multi-armed bandits, methods which allow us to zero in on the consumer's personal preferences.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355524703Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Bandits and Preference Learning.
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500 $a Source: Dissertation Abstracts International, Volume: 79-03(E), Section: B.
500 $a Adviser: Robert D. Nowak.
502 $a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017.
504 $a Includes bibliographical references
520 $a The internet revolution has brought a large population access to a vast array of infor- mation since the mid 1990s. More recently, with the advent of smartphones, it has become an essential part of our everyday life. This has lead to, among many other developments, the personalization of the online experience with great benefits to all involved. Companies have particular interest in showing products and advertisements that match what particular users are looking for, and users desire getting personalized recommendations from internet for entertainment and consumer goods that suit them as individuals. In machine learning, this is popularly achieved using the theory of the multi-armed bandits, methods which allow us to zero in on the consumer's personal preferences.
520 $a The last few decades have seen great advances in the theory and practice of multi- armed bandits exploiting either the context of the user, the context of the objects, or both. Great theoretical improvements have brought algorithms' performance close to their theoretical optimal. However, various challenges exist in the practical use of multi-armed bandits. In this thesis, we explore some of these challenges and endeavor to overcome them. First, we examine how multiple populations can be catered to si- multaneously. We then address the issue of scaling multi-armed bandits to situations where there are many arms. We also look at how to incorporate generalized linear re- ward models while maintaining computational efficiency. Finally, we address how we can use feature feedback to focus the bandits exploration to a limited subset of features. This leads to algorithms that are still tractable for high-dimensional datasets where the preferences of the user are explained by a sparse subset of them.
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538 $a Mode of access: World Wide Web
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650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
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