Predictive models of human behavior--and in particular recommender systems--learn patterns from large volumes of historical activity data, in order to make personalized predictions that adapt to the needs, nuances, and preferences of individuals. After introducing the current state-of-the-art in personalized recommendation technology, in this talk I'll cover two emerging directions in recommender systems research: (1) Work that incorporates Recommender Systems with NLP techniques, such as developing personalized language models; and (2) Work that incorporates generative modeling techniques into recommender systems, including using recommender systems to facilitate the design of new products.