View : 20 Download: 0

Efficient recommendation methods using category experts for a large dataset

Title
Efficient recommendation methods using category experts for a large dataset
Authors
Hwang, Won-SeokLee, Ho-JongKim, Sang-WookWon, YoungjoonLee, Min-Soo
Ewha Authors
이민수
SCOPUS Author ID
이민수scopus
Issue Date
2016
Journal Title
INFORMATION FUSION
ISSN
1566-2535JCR Link1872-6305JCR Link
Citation
vol. 28, pp. 75 - 82
Keywords
Recommender systemCollaborative filteringExpertPerformance evaluation
Publisher
ELSEVIER SCIENCE BV
Indexed
SCIE; SCOPUS WOS scopus
Abstract
Neighborhood-based methods have been proposed to satisfy both the performance and accuracy in recommendation systems. It is difficult, however, to satisfy them together because there is a tradeoff between them especially in a big data environment. In this paper, we present a novel method, called a CE method, using the notion of category experts in order to leverage the tradeoff between performance and accuracy. The CE method selects a few users as experts in each category and uses their ratings rather than ordinary neighbors'. In addition, we suggest CES and CEP methods, variants of the CE method, to achieve higher accuracy. The CES method considers the similarity between the active user and category expert in ratings prediction, and the CEP method utilizes the active user's preference (interest) on each category. Finally, we combine all the approaches to create a CESP method, considering similarity and preference simultaneously. Using real-world datasets from MovieLens and Ciao, we show that our proposal successfully leverages the tradeoff between the performance and accuracy and outperforms existing neighborhood-based recommendation methods in coverage. More specifically, the CESP method provides 5% improved accuracy compared to the item-based method while performing 9 times faster than the user-based method. (C) 2015 Elsevier B.V. All rights reserved.
DOI
10.1016/j.inffus.2015.07.005
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE