Supervised learning algorithms require labeled training examples from every class to engender a classification function. One of the shortcomings of this classical paradigm is that in order to learn the function accurately, a large number of labeled examples are needed. There are many situations (e.g. a new user in an online recommender system) where for every class; only a small set of labeled examples is available. Situations such as these encourage investigating about the usefulness of unlabeled examples in learning a recommender.