10/6/16 | 4:15pm | E51-315 
Reception to follow.

Abstract: We develop an innovative topic model, Hierarchically Dual Latent Dirichlet Allocation (HDLDA), which not only identifies topics in search queries and webpages, but also how the topics in search queries relate to the topics in the corresponding top search results. Using the estimates from HDLDA, a consumer's content preferences may be estimated on the fly based on their search queries. We validate our proposed approach across different product categories using an experiment in which we observe participants' content preferences, the queries they formulate, and their browsing behavior. Our results suggest that our approach can help firms extract and understand the preferences revealed by consumers through their search queries, which in turn may be used to optimize the production and promotion of online content.

Olivier Toubia

Professor
Columbia Business School

Bio: Olivier Toubia is the Glaubinger Professor of Business and faculty director of the Lang Entrepreneurship center at Columbia Business School. His research focuses on various aspects of innovation (including idea generation, preference measurement, and the diffusion of innovation), social networks and behavioral economics. He teaches a course on Customer-Centric Innovation and the core marketing course, in the MBA and Executive MBA programs. He received his MS in Operations Research and PhD in Marketing from MIT.