Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Hybrid Personalized Recommender System Using Modified Fuzzy C-Means Clustering Algorithm
Subhash K. Shinde, Uday V. kulkarni
Pages - 88 - 99     |    Revised - 31-01-2011     |    Published - 08-02-2011
Volume - 1   Issue - 4    |    Publication Date - December 2010  Table of Contents
Fuzzy C-means, Modified Fuzzy C-means, Personalized Recommender System, Content based filtering, collaborative filtering
Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. This paper proposes a novel Modified Fuzzy C-means (MFCM) clustering algorithm which is used for Hybrid Personalized Recommender System (MFCMHPRS). The proposed system works in two phases. In the first phase, opinions from the users are collected in the form of user-item rating matrix. They are clustered offline using MFCM into predetermined number clusters and stored in a database for future recommendation. In the second phase, the recommendations are generated online for active users using similarity measures by choosing the clusters with good quality rating. We propose coefficient parameter for similarity computation when weighting of the users’ similarity. This helps to get further effectiveness and quality of recommendations for the active users. The experimental results using Iris dataset show that the proposed MFCM performs better than Fuzzy C-means (FCM) algorithm. The performance of MFCMHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with fuzzy recommender system (FRS). The results obtained empirically demonstrate that the proposed MFCMHPRS performs superiorly.
CITED BY (1)  
1 Maatallah, M., & Seridi-Bouchelaghem, H. (2015). A fuzzy hybrid approach to enhance diversity in top-N recommendations. International Journal of Business Information Systems, 19(4), 505-530.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
1 G. Adomavicius and A. Tuzhilin. “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions”. IEEE Trans. Knowledge and Data Eng., 17(6): 734–74, 2005.
2 R. B. Allen. “User models: theory, method and Practice”. International Journal of Man–Machine Studies, 43(11): 27-52, 1990.
3 D. Kalles, A. Papagelis and C. Zaliagis. “Algorithmic aspects of web intelligent systems”. Web Intelligence, Springer, Berlin, 323–345, 2003.
4 J. L. Herlocker, J.A. Konstan, L. G. Terveen and J. T. Riedl. “Evaluating collaborative filtering recommender systems”. ACM Trans. on Information Systems (TOIS), 22(1): 5-53, 2005.
5 T. Hofmann. “Collaborative filtering via Gaussian probabilistic latent semantic analysis”. In Proceedings of the 26th Annual Int’l ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 145-153, 2003.
6 M. Balabanovic and Y. Sholam. “Combining content-based and collaborative recommendation”. Comm. ACM, 40(3): 23-43, 1997.
7 Chun Zeng and et al. ”Personalized Services for Digital Library”. In Proceeding of 5th International Conference on Asian Digital Libraries, pp. 252-253, 2002.
8 G. Ulrike and F. R. Daniel. “Persuasion in recommender systems.” Int’l Journal of Electronic Commerce, 11(2): 81-100, 2006.
9 S. K. Shinde and U. V. Kulkarni. “A New Approach for on Line Recommender System in Web Usage Mining”. In Proceeding Inte’l Conference on Advanced Computer Theory and Engineering, pp. 312-317, 2008.
10 S. K. Shinde and U.V. Kulkarni. “The hybrid web personalized recommendation based on web usage mining”. International Journal of Data Mining, Modeling and Management, 2(4): 315-333, 2010.
11 K. Yu, A. Schwaighofer and H. P. Kriegel. “Probabilistic Memory-Based Collaborative Filtering”. IEEE Trans. Knowledge and Data Engineering,16(1) :56-69, 2004.
12 K.W. Cheung and Ch. Tsui, “Extended latent class models for collaborative recommendation”. IEEE Trans. on Systems, Man and Cybernetics-Part A: Systems and Humans, 34(1): 143-148, 2004.
13 J. Bezdek. “Pattern Recognition with Fuzzy Objective Function Algorithms”. Plenum Press, USA, 1981.
14 J.C. Dunn. "A Fuzzy Relative of the ISODATA Process and its Use in Detecting Compact, Well Separated Clusters". Journal of Cybernetics, 3(3): 32-57, 1974.
15 Y. H. Choa and J. K. Kimb. “Application of web usage mining and product taxonomy to collaborative recommendations in e-commerce”. Expert Systems with Applications, 26(2): 233-246, 2004.
16 B. M. Kim and Q. Li. “A new approach for combining content-based and collaborative filters”. Journal of Intelligent Information Systems, 27(1), 79-91, 2006.
17 S. Vucetic. “Collaborative filtering using a regression-based approaches”. Journal of Knowledge and Information Systems, 7(1): 22-35, 2005.
18 G. L. Somlo and A. Howel, “Adaptive Lightweight Text Filtering”. In proceedings Fourth Int’l Symp. Intelligent Data Analysis, pp. 53-59, 2001.
19 Z. Huang and W. Chung. “A graph model for e-commerce recommender systems”. Journal of the American Society for Information Science and Technology, 55(3): 259–274, 2001.
20 D. Billsus and M. “Learning collaborative information filter.” In Proceeding 5th International conference on Machine Learning, pp. 46-54, 1998.
21 C. H. Basu and et al. “Recommendation as classification: Using social and content–based information in recommendation”. In Proceeding 15th International conference on Artificial Intelligence, pp. 714-720, 1998.
22 Huihong Zhou, Yijun Liu, Weiqing Zhang, and Junyuan Xie. “A Survey of Recommender System Applied in E-commerce”. Computer Application Research, 1(1): 8-12, 2004.
23 Z. Zhao and S. Bing. “An Adaptive Algorithm for Personal Recommendation”. Journal of Changchun University, 1(1): 22-29, 2005.
24 L. Teran and Andreas Meier. “A Fuzzy Recommender system for eElections”. EGOVIS 2010, LNCS, Springer-Berlin, 2(2):62-76, 2010.
Mr. Subhash K. Shinde
- India
Dr. Uday V. kulkarni
- India