Feature weighting and instance selection for collaborative filtering

Collaborative filtering uses a database about consumers' preferences to make personal product recommendations and is achieving widespread success in e-commerce nowadays. In this paper we present several feature-weighting methods to improve the accuracy of collaborative filtering algorithms. Furthermore, we propose a method to reduce the training data set by selecting only highly relevant instances. We evaluate various methods on the well-known EachMovie data set. Our experimental results show that mutual information achieves the largest accuracy gain among all feature-weighting methods. The most interesting fact is that our data reduction method even achieves an improvement of the accuracy of about 6% while speeding up the collaborative filtering algorithm by a factor of 15.