Collaborative Filtering with the Simple Bayesian Classifier

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Collaborative Filtering with the Simple Bayesian Classifier (2000)

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by Koji Miyahara , Michael J. Pazzani

Venue:	In: Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence
Citations:	42 - 1 self

BibTeX

@INPROCEEDINGS{Miyahara00collaborativefiltering,
    author = {Koji Miyahara and Michael J. Pazzani},
    title = {Collaborative Filtering with the Simple Bayesian Classifier},
    booktitle = {In: Proceedings of the 6th Pacific Rim International Conference on Artificial Intelligence},
    year = {2000},
    pages = {679--689}
}

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Abstract

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 simplicity. In this paper, we define two variants of the recommendation problem for the simple Bayesian classifier. In our approach, we calculate the similarity between users from negative ratings and positive ratings separately. We evaluated these algorithms using a database of movie recommendations and joke recommendations. Our empirical results show that one of our proposed Bayesian approaches significantly outperforms a correlation-based collaborative filtering algorithm. The other model almost outperforms as well although it shows similar performance to the correlation-based approach in some parts of our experiments.

Keyphrases

simple bayesian classifier similar taste recommendation problem similar performance correlation-based approach joke recommendation machine-learning algorithm empirical result correlation-based collaborative filtering algorithm web site bayesian approach negative rating many collaborative filtering movie recommendation positive rating various classification task

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