Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2007.14-B.7.549

Reducing Noise Using Degree of Scattering in Collaborative Filtering System  

Ko, Su-Jeong (인덕대학 컴퓨터소프트웨어과)
Publication Information
The KIPS Transactions:PartB / v.14B, no.7, 2007 , pp. 549-558 More about this Journal
Abstract
Collaborative filtering systems have problems when users rate items and the rated results depend on their feelings, as there is a possibility that the results include noise. The method proposed in this paper optimizes the matrix by excluding irrelevant ratings as information for recommendations from a user-item matrix using dispersion. It reduces the noise that results from predicting preferences based on original user ratings by inflecting the information for items and users on the matrix. The method excludes the ratings values of the utmost limits using a percentile to supply the defects of coefficient of variance and composes a weighted user-item matrix by combining the user coefficient of variance with the median of ratings for items. Finally, the preferences of the active user are predicted based on the weighted matrix. A large database of user ratings for movies from the MovieLens recommender system is used, and the performance is evaluated. The proposed method is shown to outperform earlier methods significantly.
Keywords
Collaborative Filtering System; Degree Of Scattering; Noise Reduction;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Wang, J., Vries, Arjen P. de, and Reinders, Marcel J. T., 'Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion,'In Proc. of the 29th Annual International ACM SIGIR Conference on Research & Development on Information Retrieval (SIGIR06), 2006   DOI
2 양완연, 일반통계학, 연학사, 1995
3 Mui, L., Ang, C., and Mohtashemi, M., 'A Probabilistic Model for Collaborative Sanctioning,'MIT LCS Technical Memorandum 617, 2001
4 Reddy, P. K., Kitsuregawa, P., Sreekanth, P., and Rao, S. S., 'A Graph based Approach to Extract a Neighborhood Customer Community for Collaborative Filtering,' In Proc. of Databases in Networked Information Systems, Second International Workshop, Lecture Notes in Computer Science, Springer-Verlag, 2002
5 Rijsbergen, V. and Joost, C., Information Retrieval, Butterworths, London-second edition, 1979
6 Robu, V. and Poutre , J.A. La, 'Learning the Structure of Utility Graphs Used in Multi-Issue Negotiation through Collaborative Filtering,' In Proc. of the 8th International Pacific Rim Workshop on Multi-Agent Systems (PRIMA'05), 2005
7 Massa, P. and Avesani, P., 'Trust-aware Collaborative Filtering for Recommender Systems,' In Proc. of International Conference on Cooperative Information Systems, 2004
8 Schein, A. I., Popescul, A., Ungar, L. H., and Pennock, D. M., 'Generate Models for Cold-start Recommendations,' In Proc. of the 2001 ACM SIGIR Workshop on Recommender Systems, 2001
9 Spiegel, Murray R. and Stephens, Larry J., Schaum's Outline of Statistics, McGraw-Hill, 1998
10 Basu, C., Hirsh, H., and Cohen, W. W., 'Recommendation as classification:Using social and content-based information in recommendation,' In Proc. of the Fifteenth National Conference on Artificial Intelligence, 1998
11 MovieLens collaborative filtering data set, Hppt://www.cs.umn.edu/Research/GroupLens/index.html, GROUPLENS RESEARCH PROJECT, 2000
12 Breese, John. S. and Kadie, C., 'Empirical Analysis of Predictive Algorithms for Collaborative Filtering,' In Proc. of the Conference on Uncertainty in Artificial Intelligence, Madison, WI, 1998
13 Salton, G. and McGill, M. J., Introduction to Modern Information Retrieval, McGraw-Hill, 1983
14 Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J., 'Application of Dimensionality Reduction in Recommender System-A Case Study,' In Proc. of ACM WebKDD Web Mining for E-Commerce Workshop, 2000
15 Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J., 'Item-based Collaborative Filtering Recommendation Algorithms,' In Proc. of the 10th international World Wide Web Conference(WWW10), 2001
16 Sarwar, B. M., Konstan, J. A., Borchers, A., Herlocker, J., Miller, B., and Riedl, J., 'Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System,' In Proc. of CSCW'98, 1998   DOI
17 Lee, W. S., 'Collaborative learning for recommender systems,' In Proc. of the Conference on Machine Learning, 1997
18 Herlocker, J., Konstan, J., Borchers, A., and Riedl, J., 'An Algorithmic Framework for Performing Collaborative Filtering,' In Proc. of the 1999 Conference on Research and Development in Information Retrieval, 1999   DOI
19 Herlocker, J., Konstan, J., Terveen, L., and Riedl, J., 'Evaluating Collaborative Filtering Recommender Systems,' ACM Transactions on Information Systems, Vol. 22, No. 1, 2004   DOI   ScienceOn
20 Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gordon, L., and Riedl, J., 'GroupLens:Applying Collaborative Filtering to Usenet News,' Communications of the ACM, Vol. 40, No. 3, pp. 77-87, 1997   DOI   ScienceOn