Design and user issues in personality-based recommender systems

Abstract

Recommender systems have emerged as an intelligent information filtering tool to help users effectively identify information items of interest from an overwhelming set of choices and provide personalized services. Studies show that personality influences human decision making process and interests. However, little research has ventured in incorporating it into recommender systems. The utilization of personality characteristics into recommender systems and the exploration of user perception to such systems are the focuses of my thesis. The overall goal is to develop an efficient personality-based recommender system and to arrive at a series of design guidelines from the perspective of human computer interaction. In this paper, I present my up-to-date results on a proposed personality-based music recommender prototype, user perception investigations, and my ongoing research about addressing new user problem by utilizing personality characteristics. Finally, I shall present future works.