Browse > Article
http://dx.doi.org/10.7465/jkdi.2013.24.4.803

A study of development for movie recommendation system algorithm using filtering  

Kim, Sun Ok (School of Information Communication & Broadcasting Engineering, Halla University)
Lee, Soo Yong (College of Humanities & Arts, Yonsei University)
Lee, Seok Jun (Department of MIS, Sangji University)
Lee, Hee Choon (Department of Computer Data & Information, Sangji University)
Ji, Seon Su (Department of Information Technology & Engineering, Gangneung-Wonju National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.4, 2013 , pp. 803-813 More about this Journal
Abstract
The purchase of items in e-commerce is a little bit different from that of items in off-line. The recommendation of items in off-line is conducted by salespersons' recommendation, However, the item recommendation in e-commerce cannot be recommended by salespersons, and so different types of methods can be recommended in e-commerce. Recommender system is a method which recommends items in e-commerce. Preferences of customers who want to purchase new items can be predicted by the preferences of customers purchasing existing items. In the recommender system, the items with estimated high preferences can be recommended to customers. The algorithm of collaborative filtering is used in recommender system of e-commerce, and the list of recommended items is made by estimated values, and then the list is recommended to customers. The dataset used in this research are 100k dataset and 1 million dataset in Movielens dataset. Similar results in two dataset are deducted for generalization. To suggest a new algorithm, distribution features of estimated values are analyzed by the existing algorithm and transformed algorithm. In addition, respondent'distribution features are analyzed respectively. To improve the collaborative filtering algorithm in neighborhood recommender system, a new algorithm method is suggested on the basis of existing algorithm and transformed algorithm.
Keywords
Collaborative filtering; e-commerce; items recommendation; recommender system;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Kim. J. H. and Byeon. H. S. (2011). A product recommendation system based on adjacency data. Journal of the Korean Data & Information Science Society, 22, 19-27.
2 Kim. S. H., Oh. B. H., Kim. M. J. and Yang. J. H. (2012). A movie recommendation algorithm combining collaborative filtering and content information. Journal of Korean Institute of Information Scientists and Engineers: Software and Applications, 39, 261-268.
3 Kim, S. O. (2010). The research of new algorithm to improve prediction accuracy of recommender system in electronic commerce. Journal of Korean Data & Information Science Society, 21, 185-194.
4 Kim. S. O. and Lee H. C. (2010). A study of distribution of response and rank of recommendation in collaborative filtering. Journal of the Korean Data Analysis Society, 12, 2071-2080.
5 Lee, H. C. and Lee. S. J. (2006). On the precision of the prediction of the nearest neighbor algorithm and adjusted algorithm for user-based recommender system. Journal of the Korean Data Analysis Society, 8, 1893-1904.
6 Lee, S. H. and Park, S. H. (2011). Accuracy improvement of a collaborative filtering recommender system using attribute of age. Journal of the Korea Safety Management & Science, 13, 169-177.
7 Lee. S. J., Kim. S. O. and Lee H. C. (2007a). The relationship of prediction accuracy and the run of abnormal users' ratings in collaborative filtering. Journal of the Korean Data Analysis Society, 9, 2043-2054.
8 Lee, S. J., Kim, S. O. and Lee, H. C. (2007b). A study on the interrelationship between the prediction error and the rating's pattern in collaborative. Journal of Korean Data & Information Science, 18, 659-668.
9 Linden, G., Smith, B. and York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing, 7, 76-80.   DOI   ScienceOn
10 Qinjiao, M., Boqin, F. and Shanliang, P. (2012). A study of top-n recommendation on user behavior data. 2012 IEEE International Conference on Computer Science and Automation Engineering, 2012 International Conference, 25-27 May, 582-586.
11 Wu, Q., Li, L., Li, H., Tang, F., Barolli, L. amd Luo. Y. (2012). Recommendation of more interests based on collaborative filtering, 2012 IEEE 26th International Conference on Advanced Information Networking and Applications, 2012 International Conference, 26-29 March, 191-198.
12 Yang, G. M., Lee, H. C. and Park, Y. S. (2008). The feature of preference prediction by memory-based collaborative filtering algorithm. Journal of the Korean Data Analysis Society, 10, 591-601.
13 Yu, S. J. (2012). A comprehensive performance evaluation in collaborative filtering. Journal of the Korea Society of Computer and Information, 17, 83-90.
14 Yanxiang, L., Deke, G., Fei, C. and Honghui, C. (2013). User-based clustering with top-n recommendation on cold-start problem. 2013 Third International Conference on Intelligent System Design and Engineering Applications, 2013 International Conference, 16-18 Jan, 1585-1589.