In this talk we’ll explore three lines of work at the intersection of recommender systems and natural language processing. We’ll start by introducing “traditional” recommender systems that leverage text as side-information, either to improve predictive performance or to aid interpretability. Second we’ll discuss recent methodological advances in recommendation that borrow methods from NLP as a means of modeling interaction sequences (e.g. models based on word2vec, RNNs, Transformer, etc.). Finally we’ll discuss personalized language generation, which borrows ideas from recommender systems to capture patterns of variation in text (subjectivity, context, etc.) and is driving emerging applications such as personalized dialog systems and conversational recommendation.
Julian McAuley has been a professor in the Computer Science Department at the University of California, San Diego since 2014. Previously he was a postdoctoral scholar at Stanford University after receiving his PhD from the Australian National University in 2011. His research is concerned with developing predictive models of human behavior using large volumes of online activity data.