Instance-Based Learning Algorithms
Machine Learning
Selecting typical instances in instance-based learning
ML92 Proceedings of the ninth international workshop on Machine learning
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
Artificial Intelligence Review - Special issue on lazy learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
An Information-Theoretic Approach to Neural Computing
An Information-Theoretic Approach to Neural Computing
Artificial Intelligence Review - Special issue on lazy learning
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Feature Weighting and Instance Selection for Collaborative Filtering
DEXA '01 Proceedings of the 12th International Workshop on Database and Expert Systems Applications
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Privacy conflicts in CRM services for online shops: a case study
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Data sparsity issues in the collaborative filtering framework
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
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Collaborative filtering uses a database about consumers' preferences to make personal product recommendations and is achieving widespread success in both E-Commerce and Information Filtering Applications nowadays. However, the traditional collaborative filtering algorithms do not scale well to the ever-growing number of consumers. The quality of the recommendation also needs to be improved in order to gain more trust from the consumers. In this paper, we present a novel method to improve the scalability and the accuracy of the collaborative filtering algorithm. We introduce an information theoretic approach to measure the relevance of a consumer (instance) for predicting the preference for the given product (target concept). The proposed method reduces the training data set by selecting only highly relevant instances. Our experimental evaluation on the well-known EachMovie data set shows that our method doesn't only significantly speed up the prediction, but also results in a better accuracy.