國立虎尾科技大學 |

Leveraging Collective Intelligence in Recommender System.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Leveraging Collective Intelligence in Recommender System./
Author:	Chang, Shuo.
Description:	1 online resource (136 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781369190465

Leveraging Collective Intelligence in Recommender System.
Chang, Shuo.

Leveraging Collective Intelligence in Recommender System. - 1 online resource (136 pages)

Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.

Thesis (Ph.D.)--University of Minnesota, 2016.

Includes bibliographical references

Recommender systems, since their introduction 20 years ago, have been widely deployed in web services to alleviate user information overload. Driven by business objectives of their applications, the focus of recommender systems has shifted from accurately modeling and predicting user preferences to offering good personalized user experience. The later is difficult because there are many factors, e.g., tenure of a user, context of recommendation and transparency of recommender system, that affect users' perception of recommendations. Many of these factors are subjective and not easily quantifiable, posing challenges to recommender algorithms.

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

Mode of access: World Wide Web

ISBN: 9781369190465Subjects--Topical Terms:

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

554714
Electronic books.

Leveraging Collective Intelligence in Recommender System.
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100 1 $a Chang, Shuo. $3 1188555
245 1 0 $a Leveraging Collective Intelligence in Recommender System.
264 0 $c 2016
300 $a 1 online resource (136 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 78-02(E), Section: B.
500 $a Adviser: Loren Terveen.
502 $a Thesis (Ph.D.)--University of Minnesota, 2016.
504 $a Includes bibliographical references
520 $a Recommender systems, since their introduction 20 years ago, have been widely deployed in web services to alleviate user information overload. Driven by business objectives of their applications, the focus of recommender systems has shifted from accurately modeling and predicting user preferences to offering good personalized user experience. The later is difficult because there are many factors, e.g., tenure of a user, context of recommendation and transparency of recommender system, that affect users' perception of recommendations. Many of these factors are subjective and not easily quantifiable, posing challenges to recommender algorithms.
520 $a When pure algorithmic solutions are at their limits in providing good user experience in recommender systems, we turn to the collective intelligence of human and computer. Computer and human are complementary to each other: computers are fast at computation and data processing and have accurate memory; humans are capable of complex reasoning, being creative and relating to other humans. In fact, such close collaborations between human and computer have precedent: after chess master Garry Kasparov lost to IBM computer ''Deep Blue'', he invited a new form of chess --- advanced chess, in which human player and a computer program teams up against such pairs.
520 $a In this thesis, we leverage the collective intelligence of human and computer to tackle several challenges in recommender systems and demonstrate designs of such hybrid systems. We make contributions to the following aspects of recommender systems: providing better new user experience, enhancing topic modeling component for items, composing better recommendation sets and generating personalized natural language explanations. These four applications demonstrate different ways of designing systems with collective intelligence, applicable to domains other than recommender systems. We believe the collective intelligence of human and computer can power more intelligent, user friendly and creative systems, worthy of continuous research effort in future.
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
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Minnesota. $b Computer Science. $3 1180176
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10164554 $z click for full text (PQDT)