Selecting relevant instances for efficient and accurate collaborative filtering
Proceedings of the tenth international conference on Information and knowledge management
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Similarity Measure and Instance Selection for Collaborative Filtering
International Journal of Electronic Commerce
| Hi-index | 0.00 |
Abstract: 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 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.