- Volume 20 Issue 3
The researcher analyzes the relationship between the number of neighbors and the prediction accuracy in the preference prediction process using collaborative filtering system. The number of neighbors who are involved in the preference prediction process are divided into four groups. Each group shows a little difference in the preference prediction. By using prediction error averages in each group, linear functions are suggested. Through the result of this study, the accuracy of preference prediction can be raised when using linear functions by using the number of neighbors in the suggested system.
- 김용수 (2005). <전자상거래에서 고객의 탐색 및 행동 패턴을 고려한 추천시스템의 개발>, 박사학위논문, 한국과학기술원, 대전.
- 이희춘, 이석준 (2006). 사용자 기반 추천시스템에서 근접이웃 알고리즘과 수정알고리즘의 예측 정확도에 관한 연구. <한국자료분석학회지>, 8, 1893-1904.
- 한국인터넷진흥원 (2007). <2007 한국인터넷백서>, 한국인터넷진흥원, 서울.
- Breese, J., Heckerman, D. and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth Annual Conference on Uncertainty in Artificial Intelligence, 43-52.
- Herlocker, J., Konstan, J., Borchers, A. and Riedl, J. (1999). An algorithm framework for performing collaborative filtering. In Proceedings of the 1999 Conference on Research and Development in Information Retrieval, 230-237.
- Herlocker, J., Konstan, J. and Riedl, J. (2002). An empirical analysis of design choices in neighborhood based collaborative filtering algorithms. Information Retrieval, 5, 287-310. https://doi.org/10.1023/A:1020443909834
- Herlocker, J., Komstan J., Terveen, L. and Riedle, J. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22, 5-53. https://doi.org/10.1145/963770.963772
- Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L. and Riedl, J. (1997). GroupLens: Applying collaborative filtering to usenet news. Communications of the ACM, 40, 77-87. https://doi.org/10.1145/245108.245126
- Lee, H. C. (2006). Improved algorithm for user based recommender system. Journal of Korean Data & Information Science Society, 17, 717-726.
- Lee, H. C., Lee, S. J. and Chung, Y. J. (2007). A study on the improved collaborative filtering algorithm for recommender system. SERA 2007. 5th ACIS International Conference, 297-304.
- Lee, S. J., Kim, S. O. and Lee, H. C. (2007). Pre-evaluation for detecting abnormal users in recommender system. Journal of Korean Data & Information Science Society, 18, 619-628.
- Linden, G., Smith, B. and York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7, 76-80. https://doi.org/10.1109/MIC.2003.1167344
- Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P. and Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, 175-186.
- Riedl, J. and Konstan. J. (2002). Word of mouse: The marketing power of collaborative filtering, Warner Books, New York.
- Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference, 285-295.
- Schafer, J., Konstan, J. and Riedle, J. (2001). E-commerce recommendation applications. Journal of Data Mining and Knowledge Discovery, 5, 115-152. https://doi.org/10.1023/A:1009804230409
- Shardanand, U. and Maes, P. (1995). Social information filtering: Algorithms for automating 'word of mouth'. In Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems, 210-217.