Collaborative Filtering with the Simple Bayesian Classifier

@inproceedings{Miyahara2000CollaborativeFW,
  title={Collaborative Filtering with the Simple Bayesian Classifier},
  author={Koji Miyahara and Michael J. Pazzani},
  booktitle={PRICAI},
  year={2000}
}
Many collaborative filtering enabled Web sites that recommend books, CDs, movies, videos and so on, have become very popular on Internet. They recommend items to a user based on the opinions of other users with similar tastes. In this paper, we discuss an approach to collaborative filtering based on the simple Bayesian classifier. The simple Bayesian classifier is one of the most successful supervised machine-learning algorithms. It performs well in various classification tasks in spite of its… CONTINUE READING
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Showing 1-10 of 15 references

Jester 2.0: A New Linear-Time Collaborative Filtering Algorithm Applied to Jokes

  • D. Gupta, M. Digiovanni, H. Narita, K. Goldberg
  • Workshop on Recommender Systems Algorithms and…
  • 1999
1 Excerpt

EachMovie collaborative filtering data set

  • P. McJonese
  • DEC Systems Research Center
  • 1997
2 Excerpts

Machine Learning. MacGraw-Hill, New York

  • T. Mitchell
  • 1997

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