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Recommender System using Implicit Trust-enhanced Collaborative Filtering
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Kyoung-jae Kim (Business School, Dongguk University_Seoul)
Youngtae Kim (Department of Management Information Systems, Graduate School, Dongguk University_Seoul)
Vol. 19, No. 4, Page: 1 ~ 10
10.13088/jiis.2013.19.4.001
Keywords
Implicit evaluation, Sparsity, Recommender system, Collaborative filtering, Customer Relationship Management
Abstract
Personalization aims to provide customized contents to each user by using the user’s personal preferences. In this sense, the core parts of personalization are regarded as recommendation technologies, which can recommend the proper contents or products to each user according to his/her preference. Prior studies have proposed novel recommendation technologies because they recognized the importance of recommender systems. Among several recommendation technologies, collaborative filtering (CF) has been actively studied and applied in real-world applications. The CF, however, often suffers sparsity or scalability problems. Prior research also recognized the importance of these two problems and therefore proposed many solutions. Many prior studies, however, suffered from problems, such as requiring additional time and cost for solving the limitations by utilizing additional information from other sources besides the existing user-item matrix. This study proposes a novel implicit rating approach for collaborative filtering in order to mitigate the sparsity problem as well as to enhance the performance of recommender systems. In this study, we propose the methods of reducing the sparsity problem through supplementing the user-item matrix based on the implicit rating approach, which measures the trust level among users via the existing user-item matrix. This study provides the preliminary experimental results for testing the usefulness of the proposed model.
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김경재 (동국대학교_서울 경영학부 교수)
김영태 (동국대학교_서울 일반대학원 경영정보학과 석사과정)
Keywords
내재적 평가, 희박성, 추천시스템, 협업필터링, 고객관계관리
Abstract
개인화는 개인적인 기호를 바탕으로 각 사용자에게 맞춤화된 컨텐츠를 제공하는 것을 목표로 한다. 이러한 관점에서, 개인화의 핵심적인 부분은 각 사용자의 기호에 적합한 컨텐츠나 상품을 추천할 수 있는 추천기술이라 할 수 있다. 선행연구들은 추천시스템의 중요성을 인지하고 새로운 추천기술을 제안하여 왔다. 여러 추천기술들 중에서 협업필터링은 실무에서 활발하게 연구되고 활용되어 왔다. 그러나, 협업필터링은 종종 희박성 또는 확장성 문제를 겪게 된다. 선행연구들 역시 이 두 가지 문제점의 중요성을 인지하고 그에 대한 여러 가지 해결방안들을 제안하였다. 하지만, 여러 선행연구들은 기존의 사용자-상품 매트릭스 외에 다른 원천들로부터 생성된 추가적인 정보를 이용함으로써 문제점들을 해결하려 함으로 인하여 추가적인 시간과 비용을 요하는 다른 문제를 야기하였다. 본 연구에서는 희박성 문제를 완화하고 추천시스템의 성능을 개선하기 위하여 협업필터링을 위한 새로운 내재적 평가방법을 제안한다. 즉, 본 연구에서는 기존 사용자-상품 매트릭스를 이용하여 사용자 간의 신뢰수준을 측정할 수 있는 내재적 평가법에 기반한 사용자-상품 매트릭스의 보완을 통해 희박성 문제를 완화할 수 있는 방안을 제안한다. 또한, 본 연구에서는 제안하는 방안의 유용성을 평가하기 위한 탐색적 실험 결과를 제공한다.
Cite this article
JIIS Style
Kim, K.-j., and Y. Kim, "Recommender System using Implicit Trust-enhanced Collaborative Filtering", Journal of Intelligence and Information Systems, Vol. 19, No. 4 (2013), 1~10.

IEEE Style
Kyoung-jae Kim, and Youngtae Kim, "Recommender System using Implicit Trust-enhanced Collaborative Filtering", Journal of Intelligence and Information Systems, vol. 19, no. 4, pp. 1~10, 2013.

ACM Style
Kim, K.-j., and Kim, Y., 2013. Recommender System using Implicit Trust-enhanced Collaborative Filtering. Journal of Intelligence and Information Systems. 19, 4, 1--10.
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@article{Kim:JIIS:2013:548,
author = {Kim, Kyoung-jae and Kim, Youngtae},
title = {Recommender System using Implicit Trust-enhanced Collaborative Filtering},
journal = {Journal of Intelligence and Information Systems},
issue_date = {December 2013},
volume = {19},
number = {4},
month = Dec,
year = {2013},
issn = {2288-4866},
pages = {1--10},
url = {http://dx.doi.org/10.13088/jiis.2013.19.4.001 },
doi = {10.13088/jiis.2013.19.4.001},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Implicit evaluation, Sparsity, Recommender system, Collaborative filtering and Customer Relationship Management },
}
%0 Journal Article
%1 548
%A Kyoung-jae Kim
%A Youngtae Kim
%T Recommender System using Implicit Trust-enhanced Collaborative Filtering
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 19
%N 4
%P 1-10
%D 2013
%R 10.13088/jiis.2013.19.4.001
%I Korea Intelligent Information System Society