A collaborative filtering algorithm for sparse data.
Nguyễn Duy Phương, Tu Minh Phuong · Journal of Computer Science and Cybernetics · 2012
Collaborative filtering is a technique to predict the utility of items for a particular user by exploiting the behavior patterns of a group of users with similar preferences.This technique has been widely used for recommender systems and has a number of useful applications in e-commerce.In this paper, we present a collaborative filtering method based on an multi-task learning algorithm that was designed for pattern recognition .The method formulates the collaborative filtering problem as classification problems and performs classification for all users simultaneously by using a modified boosting algorithm.This allows sharing common features among different classification tasks and thus reduces the negative effect of data sparseness.Experimental results show the effectiveness of the proposed method in comparison with other methods, especially when data are sparse.