GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Effects of Singular Value Decomposition on Collaborative Filtering
The Effects of Singular Value Decomposition on Collaborative Filtering
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
IEEE Transactions on Knowledge and Data Engineering
Using Ontology to Enhance Collaborative Recommendation Based on Community
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
A random walk method for alleviating the sparsity problem in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
An Improved Trust Metric for Trust-Aware Recommender Systems
ETCS '09 Proceedings of the 2009 First International Workshop on Education Technology and Computer Science - Volume 01
Recommendation as link prediction: a graph kernel-based machine learning approach
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Hydra: a hybrid recommender system [cross-linked rating and content information]
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Personalization in e-commerce applications
The adaptive web
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
Extended latent class models for collaborative recommendation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper, an integrated recommendation approach using Radial Basis Function Network (RBFN) and Collaborative Filtering (CF) is proposed. Radial basis function network is a neural network approximation method used to improve the accuracy of the recommendations. The proposed system RBFN_KFCM has offline and online phases. During offline, the system uses RBFN for smoothing and kernel fuzzy c-means (KFCM) method for clustering. During online recommendation, KFCM based approach is proposed. At the end of each session the clusters are updated by replacing the smoothed rating by the original rating. A comparison is made with benchmark recommender systems such as item-based, user-based systems, singular value decomposition (SVD) and also popular machine learning techniques such as Support Vector Machine (SVM), Multilayer Perceptron (MLP) using backpropagation algorithm, in terms of accuracy, decision-support measures and computational time. Empirical evaluation is carried out by using movielens dataset.