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Published in:
2005 | OriginalPaper | Chapter
A Naive Statistics Method for Electronic Program Guide Recommendation System
Authors : Jin An Xu, Kenji Araki
Published in: Computational Intelligence and Security
Publisher: Springer Berlin Heidelberg
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In this paper, we propose a naive statistics method for constructing a personalized recommendation system for the Electronic Program Guide (EPG). The idea is based on a primitive approach of N-gram to acquire nouns and compound nouns as prediction features, and then to combine the
$\it{tf\cdot idf}$
weighting to predict user favorite programs. Our approach unified feedback process, system can incrementally update the vector of extracted features and their scores. It was proved that our system has good accuracy and dynamically adaptive capability.