Abstract
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.
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Xu, J.A., Araki, K. (2005). A Naive Statistics Method for Electronic Program Guide Recommendation System. In: Hao, Y., et al. Computational Intelligence and Security. CIS 2005. Lecture Notes in Computer Science(), vol 3801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596448_64
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DOI: https://doi.org/10.1007/11596448_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30818-8
Online ISBN: 978-3-540-31599-5
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