A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonneg-ativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in… (More)
We describe some of the ongoing projects at Yahoo! Research Labs that involve recommender systems. We discuss recommender systems related problems and solutions relevant to Yahoo!'s business.