selecting relevant instances for efficient and accurate collaborative filtering
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.
collaborative filtering data mining instance selection