Recommender Systems with Personality

Amos Azaria, Jason I. Hong

Abstract: We believe that in the future, the most common form of recommender systems will be present in a personal assistant. We claim that such an intelligent agent must be personal, i.e., know its user's preferences and recommend relevant content, a dynamic learner, instructable, supportive and affable. We describe the current state of the art and the challenges which should be addressed in each of these agent properties and provide examples of how we expect future personal agents to convey these properties.