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
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
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
A study of methods for normalizing user ratings in collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
The decision-theoretic interactive video advisor
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Entropy based fuzzy C-Mean for item-based collaborative filtering
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Membership enhancement with exponential fuzzy clustering for collaborative filtering
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Optimal associative neighbor mining using attributes for ubiquitous recommendation systems
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Privacy-preserving SOM-based recommendations on horizontally distributed data
Knowledge-Based Systems
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In this paper we present the recommender systems that use the k-means clustering method in order to solve the problems associated with neighbor selection. The first method is to solve the problem in which customers belong to different clusters due to the distance-based characteristics despite the fact that they are similar customers, by properly converting data before performing clustering. The second method explains the k-prototype algorithm performing clustering by expanding not only the numeric data but also the categorical data. The experimental results show that better prediction quality can be obtained when both methods are used together.