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Augmenting knowledge reuse using col...

Im, Il.

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Augmenting knowledge reuse using collaborative filtering systems.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Augmenting knowledge reuse using collaborative filtering systems./
Author:	Im, Il.
Description:	181 p.
Notes:	Chair: Alexander Hars.
Contained By:	Dissertation Abstracts International62-09A.
Subject:	Business Administration, Management. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3027730
ISBN:	9780493397870

Augmenting knowledge reuse using collaborative filtering systems.
Im, Il.

Augmenting knowledge reuse using collaborative filtering systems. - 181 p.

Chair: Alexander Hars.

Thesis (Ph.D.)--University of Southern California, 2001.

In today's networked business environment, with the endless increase in available information, relevant information is becoming more and more difficult to find. Collaborative filtering (CF) generates recommendations for users based on others' evaluations. CF has great potential to improve information search and knowledge reuse.

ISBN: 9780493397870Subjects--Topical Terms:

626628
Business Administration, Management.

Augmenting knowledge reuse using collaborative filtering systems.
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020 $a 9780493397870
035 $a (UnM)AAI3027730
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040 $a UnM $c UnM
100 1 $a Im, Il. $3 1288660
245 1 0 $a Augmenting knowledge reuse using collaborative filtering systems.
300 $a 181 p.
500 $a Chair: Alexander Hars.
500 $a Source: Dissertation Abstracts International, Volume: 62-09, Section: A, page: 3107.
502 $a Thesis (Ph.D.)--University of Southern California, 2001.
520 $a In today's networked business environment, with the endless increase in available information, relevant information is becoming more and more difficult to find. Collaborative filtering (CF) generates recommendations for users based on others' evaluations. CF has great potential to improve information search and knowledge reuse.
520 $a Previous studies have focused on the improvement of CF algorithms. Little research has been done on the effect of users and types of product domains on the performance of CF systems. In this study, four factors---product domain, user characteristics, user's search mode, and number of users---that are expected to affect the accuracy of CF systems---were identified and investigated. Five main hypotheses were developed from previous CF research and related literature. (H1) The performance of a CF system increases as the number of users increases. ( H2) Reference group size affects the performance of a CF system. (H3) The performance of CF differs depending on the search mode of users. ( H4) The performance of a CF system is affected by the characteristics of users. (H5) The performance of a CF system is affected by the patterns of users' preference clusters.
520 $a These hypotheses were tested using data collected from two experiments in two different product domains, movies and research papers. It was shown that reaching critical mass takes longer in a consumer domain than in a knowledge-intensive domain. Users' search mode and knowledge in a domain are important determinants of the accuracy of a CF system.
520 $a Adapting the theories in the 'mental model' and 'cognitive map' areas, this study provides theoretical explanations on how CF helps users find relevant information and why the accuracy of CF systems differ in different search modes and with the user's level of knowledge.
520 $a The results show that managers need to invest in collecting enough initial data to reach critical mass quickly. System implementers have to design CF systems customized for different domains and user groups. A successful CF system needs to be designed to handle multiple modes of search, even within a domain and user group.
590 $a School code: 0208.
650 4 $a Business Administration, Management. $3 626628
650 4 $a Computer Science. $3 626642
690 $a 0454
690 $a 0984
710 2 0 $a University of Southern California. $3 700129
773 0 $t Dissertation Abstracts International $g 62-09A.
790 $a 0208
790 1 0 $a Hars, Alexander, $e advisor
791 $a Ph.D.
792 $a 2001
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3027730