Content classification and recommendation techniques for viewing
electronic programming guide on a portable device
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 21 (2):
375-395 (March 2007)International Workshop on Web Personalization, Recommender Systems and
Intelligent User Interfaces, Reading, ENGLAND, OCT, 2005.Abstract
With the merge of digital television (DTV) and the exponential growth
of broadcasting network, an overwhelmingly amount of information has
been made available to a consumer's home. Therefore, how to provide
consumers with the right amount of information becomes a challenging
problem. In this paper, we propose an electronic programming guide
(EPG) recommender based on natural language processing techniques, more
specifically, text classification. This recommender has been
implemented as a service on a home network that facilitates the
personalized browsing and recommendation of TV programs on a portable
remote device. Evaluations of our Maximum Entropy text classifier were
performed on multiple categories of TV programs, and a near 80\%
retrieval rate is achieved using a small set of training data.
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