AIMED: a personalized TV recommendation system

Authors:
Shang H. Hsu;Ming-Hui Wen;Hsin-Chieh Lin;Chun-Chia Lee;Chia-Hoang Lee
Affiliations:
Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan;Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan;Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan;Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, Taiwan;Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
Venue:
EuroITV'07 Proceedings of the 5th European conference on Interactive TV: a shared experience
Year:
2007

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Abstract

Previous personalized DTV recommendation systems focus only on viewers' historical viewing records or demographic data. This study proposes a new recommending mechanism from a user oriented perspective. The recommending mechanism is based on user properties such as Activities, Interests, Moods, Experiences, and Demographic information--AIMED. The AIMED data is fed into a neural network model to predict TV viewers' program preferences. Evaluation results indicate that the AIMED model significantly increases recommendation accuracy and decreases prediction errors compared to the conventional model.