Improving the quality of the personalized electronic program guide

  • O'Sullivan D
  • Smyth B
  • Wilson D
 et al. 
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As Digital TV subscribers are offered more and more channels, it is becoming more and more difficult for them to locate the right programme infor- mation at the right time. The so-called personalized Electronic Programme Guide (pEPG) is one solution to this problem that leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the availability of limited profiling information is a key limiting factor in such personalized recommender systems such as pEPGs. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this paper we address this problem in the DTV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automati- cally discovery by mining ratings-based profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale state-of-the-art online systems—PTVPlus, a personalized TV listings portal and F´ ıschl´ ar, an online digital video library system

Author-supplied keywords

  • Case-based reasoning
  • Collaborative filtering
  • Data mining
  • Digital TV
  • Personalization
  • Similarity maintenance

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