AIMED - A personalized TV recommendation system

Shang-Hwa Hsu, Ming-Hui Wen, Hsin-Chieh Lin, Chun-Chia Lee, Chia-Hoang Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

65 Scopus citations

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.
Original languageEnglish
Title of host publicationINTERACTIVE TV: A SHARED EXPERIENCE, PROCEEDING
PublisherSpringer-Verlag Berlin Heidelberg
Pages166-+
Volume4471
ISBN (Print)978-3-540-72558-9
DOIs
StatePublished - 24 May 2007

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • TV program recommendation system
  • Predictor
  • personal information
  • activity
  • Lifestyles
  • interest
  • mood

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  • Cite this

    Hsu, S-H., Wen, M-H., Lin, H-C., Lee, C-C., & Lee, C-H. (2007). AIMED - A personalized TV recommendation system. In INTERACTIVE TV: A SHARED EXPERIENCE, PROCEEDING (Vol. 4471, pp. 166-+). (Lecture Notes in Computer Science). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-540-72559-6_18