Personalization: Improving Ease-of-Use, Trust and Accuracy of a TV Show Recommender

Abstract

The plethora of content available to TV viewers has become overwhelming creating a need to help the viewers to find the programs that are the most interesting for them to watch. Towards this end we are developing a personalization system that recommends TV shows to users based on the knowledge of their preferences. For a quicker adoption of the personalization system by users, there is a need for the system to be easy to use, provide recommendations with high accuracy and build trust in the recommendations delivered. The user interface and recommender engine work hand in hand in order to provide all three items. In this paper we describe our system and show how it addresses each of the three issues mentioned.