Scalable learning algorithms for distributed collaborative filtering and large-scale multi-class classification

The tremendous production of data, known as the big data phenomena, has overturned the classical view in science and information technology domains, notably in the statistical machine learning field. In many real problems, particularly associated with the Internet but not only, massive data streams are continuously produced. In this thesis we are interested in the study of learning algorithms that can pass the scale; we are more particularly interested in multi-class classification and Collaborative filtering. The latter is popularly used by internet vendors such as Amazon, Netflix, Yahoo! and others. However, with increasing number of users and items, attaining a high prediction accuracy is a computationally challenging problem. So, for this application we introduce an asynchronous distributed framework to cope up the large-scale dataset challenge. Additionally, we propose a novel regularization parameter to take into account the interaction of similar users/items when estimating the predicted ratings.