GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A hybrid framework for similarity-based recommendations
International Journal of Business Intelligence and Data Mining
Expert Systems with Applications: An International Journal
Two-way cooperative prediction for collaborative filtering recommendations
Expert Systems with Applications: An International Journal
Adaptive algorithm based on clustering techniques for custom reading plans
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
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In Electronic Commerce it is not easy for customers to find the best suitable goods as more and more information is placed on line. In order to provide information of high value a customized recommender system is required. One of the typical information retrieval techniques for recommendation systems in Electronic Commerce is collaborative filtering which is based on the ratings of other customers who have similar preferences. However, collaborative filtering may not provide high quality recommendation because it does not consider customer's preferences on the attributes of an item and the preference is calculated only between a pair of customers. In this paper we present an improved recommendation algorithm for collaborative filtering. The algorithm uses the K-Means Clustering method to reduce the search space. It then utilizes a graph approach to the best cluster with respect to a given test customer in selecting the neighbors with higher similarities as well as lower similarities. The graph approach allows us to exploit the transitivity of similarities. The algorithm also considers the attributes of each item. In the experiment the EachMovie dataset of the Digital Equipment Corporation has been used. The experimental results show that our algorithm provides better recommendation than other methods.