Evaluation of Usefulness of Unlabeled Data in Learning a Recommender

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.

Provided by: Serials Publiations Topic: Big Data Date Added: Mar 2014 Format: PDF

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