Consumer Preference Exploration with Unexpected Recommender Systems

Information Systems and Operations Management

Speaker : Alexander TUZHILIN (Stern School of Business, NYU)

Room Bernard Ramanantsoa

Abstract

One of the key issues with recommender systems constitutes the filter bubble phenomenon when consumers are presented mostly with familiar and repeated types of recommendations which isolates them from the less familiar world of broader choices and options. To address this problem, this talk presents a novel approach to providing unexpected recommendations that surprise consumers by significantly deviating from their typical expectations. In particular, the unexpectedness objective is introduced in this talk using certain deep learning methods and then is subsequently incorporated into the utility function in a personalized manner that captures heterogeneous consumer propensity to seek product variety. It will also be shown that it is desirable to provide more unexpected recommendations to variety-seekers, and vice versa. It will be shown that the proposed model significantly increases various business performance metrics vis-à-vis the currently used methods.