國立虎尾科技大學 |

Learning to Rank for Retrieval and Recommendation.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Learning to Rank for Retrieval and Recommendation./
Author:	Lim, Daryl Kah Hian.
Description:	1 online resource (163 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Contained By:	Dissertation Abstracts International78-08B(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781369684025

Learning to Rank for Retrieval and Recommendation.
Lim, Daryl Kah Hian.

Learning to Rank for Retrieval and Recommendation. - 1 online resource (163 pages)

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Automated systems which can accurately surface relevant content for a given query have become an indispensable tool for navigating large and complex data collections which grow in number every day. At the core of these retrieval systems is the ranking task, i.e., ordering a set of items by their predicted relevance to a query. In recent years, one prominent approach to solving the ranking problem has been learning to rank, in which machine learning methods are employed to learn predictive models that can generate good rankings. This dissertation proposes machine learning algorithms for efficient and effective retrieval of relevant content with a focus on two problem settings: query-by-example retrieval and collaborative filtering with implicit feedback.

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

Mode of access: World Wide Web

ISBN: 9781369684025Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Learning to Rank for Retrieval and Recommendation.
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020 $a 9781369684025
035 $a (MiAaPQ)AAI10250890
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035 $a AAI10250890
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Lim, Daryl Kah Hian. $3 1182148
245 1 0 $a Learning to Rank for Retrieval and Recommendation.
264 0 $c 2017
300 $a 1 online resource (163 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500 $a Adviser: Gert Lanckriet.
502 $a Thesis (Ph.D.) $c University of California, San Diego $d 2017.
504 $a Includes bibliographical references
520 $a Automated systems which can accurately surface relevant content for a given query have become an indispensable tool for navigating large and complex data collections which grow in number every day. At the core of these retrieval systems is the ranking task, i.e., ordering a set of items by their predicted relevance to a query. In recent years, one prominent approach to solving the ranking problem has been learning to rank, in which machine learning methods are employed to learn predictive models that can generate good rankings. This dissertation proposes machine learning algorithms for efficient and effective retrieval of relevant content with a focus on two problem settings: query-by-example retrieval and collaborative filtering with implicit feedback.
520 $a In the first part of the dissertation, two algorithms that learn a Mahalanobis distance metric optimized with respect to a ranking loss are proposed. The first method suppresses noisy data dimensions effectively during training, while the second method scales to large, high-dimensional data sets while attaining strong retrieval performance on top-of-the-ranking performance metrics. In the second part of the dissertation, a new performance measure for evaluating the recommendation quality of collaborative filtering recommender systems that utilize implicit user feedback is proposed. A feature extraction-based recommender system is then developed and optimized for this performance measure, with extensive empirical evaluations conducted to demonstrate the efficacy of the proposed approach.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, San Diego. $b Electrical Engineering (Intelsys, Robotics and Cont). $3 1182149
773 0 $t Dissertation Abstracts International $g 78-08B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10250890 $z click for full text (PQDT)