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A Study on the Model based on the Number of Responses and Rank Fitting Accuracy in Collaborative Filtering
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협력적 필터링에서 응답수와 순위일치도와의 모형에 대한 연구

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Type
Academic journal
Author
Journal
The Korean Data Analysis Society Journal of The Korean Data Analysis Society Journal of The Korean Data Analysis Society 제19권 제4호 KCI Accredited Journals
Published
2017.1
Pages
1,907 - 1,915 (9page)

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A Study on the Model based on the Number of Responses and Rank Fitting Accuracy in Collaborative Filtering
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Abstract· Keywords

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Collaborative filtering is one of the data processing methods that constructs a recommendation system that selects the products that customers prefer by using the relationships between customers or products based on the ratings of customer 's preference for purchased products. This study focuses on collaborative filtering based on neighbors, which considers the affinity similarity among customers. The performance of this method is known to be influenced by the number of preferences that customers have evaluated for previously purchased products. Previous studies have attempted to improve the performance of this method by assigning an arbitrary number to the number of preference responses evaluated by customers. This study also aims at improving performance, especially the rank fitting accuracy of neighborhood-based collaborative filtering. We analyzed the relationship between the number of preference responses evaluated by customers and the rank fitting accuracy, and suggested a model that can improve the rank fitting accuracy, and then the rank fitting accuracy of the method applying the proposed model was evaluated. As a result, when the model presented in this study was applied, the rank fitting accuracy was improved, and when the number of customer's response was small, the rank fitting accuracy was improved significantly. We used the 100k of MoveLens data for this study, which consisted of 10,000 responses, which 943 customers rated 1682 films.
#순위일치도 #협력적 필터링 #응답수 #빅데이터. #Collaborative filtering #Rank fitting accuracy #Number of responses #Big data.

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References (9)

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Herlocker, J. L. / 2002 / An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms / Information Retrieval 5 (4) : 287 ~ 310 google schola Resnick, P. / 1994 / Grouplens: An open architecture for collaborative filtering of netnews / Proceedings of the ACM Congerence on Computer Supported Cooperative Work : 175 ~ 186 google schola 구지현 / 2014 / 전자상거래에서 개인화서비스 향상을 위한 협력적 필터링 추천시스템 연구 / 박사 / 한국외국어대학교 대학원 google schola 구지현 / 2015 / 고객의 상품경험의 다양성과 상품선택의 대중성이 추천시스템에 미치는 영향 / Journal of The Korean Data Analysis Society 17 (6) : 3097 ~ 3106 dbpia 구지현 / 2015 / 협력적 필터링에서 장르분포를 이용한 추천순위 개선 / Journal of The Korean Data Analysis Society 17 (2) : 913 ~ 925 dbpia

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