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An Alternative Method of Missing Data for Improving Rank Fitting in Collaborative Filtering
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협력적 필터링에서 순위일치도 향상을 위한 결측치 대체 방법

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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 제18권 제1호 KCI Accredited Journals
Published
2016.1
Pages
207 - 216 (10page)

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An Alternative Method of Missing Data for Improving Rank Fitting in Collaborative Filtering
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Abstract· Keywords

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Collaborative filtering is the algorithm to predict customers’ preference of products they has not purchased by using the rating scores for goods they bought before, and has been used as a tool to provide personalized service to customers in e-commerce. General methods for evaluating the accuracy of the algorithm are MAE and RMSE to measure the accuracy of the prediction, and top-n and rank fitting to measure the accuracy of the sequence. They are influenced by the number of co-pairs or neighbors of preferences evaluating customers. When the rating data is sparsity, this algorithm has the disadvantage that the accuracy of prediction and rank fitting are low. Rank fitting is important for the exact recommendation in product recommendation. In this study, we conducted a study on the new alternative methods for missing preference ratings to improve rank fitting. We improved rank fitting by using the methods increasing the number of neighbors and co-pairs as alternative method of missing data. Also, we identified to further increase rank fitting by using the weights of co-pair. This method will be able to solve the early disadvantages of recommendation system in sparsity.
#순위일치도 #추천시스템 #협력적 필터링 #희소성. #rank fitting #recommender system #collaborative filtering #sparsity.

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References (20)

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Adomavicius, G. / 2005 / Toward the next generation of recommender systems : a survey of the state-of-the-art and possible extensions / IEEE Transactions on Knowledge and Data Engineering 17(6):734~749 google schola Bobadilla, J. / 2013 / Recommender systems survey / Knowledge-Based Systems 46:109~132 google schola 최흥식 / 2015 / 순위일치도와 응답자 분포의 형상에 대한 연구 / Journal of The Korean Data Analysis Society 17(1):161~171 dbpia Gogna, A. / 2015 / A comprehensive recommender system model : improving accuracy for both warm and cold start users / Digital Object Identifier IEEE Access 3:2803~2813 google schola Herlocker, J. / 1999 / An algorithmic framework for performing collaborative filtering / Proceedings of the 1999 Conference on Research and Development in Information Retrieval :230~237 google schola

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