Collaborative Filtering with the Simple Bayesian Classifier

By Koji Miyahara, Michael J. Pazzani. Pacific Rim International Conference on Artificial Intelligence, 2000

339 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2f90e50026d7815e199c57e0d0123da856e56503

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In Management Science and Operations Research

Citations in the corpus

Data Mining Methods for Recommender Systems (2015, methodology): “Miyahara and Pazzani [48] implement a recommender system based on a Naive Bayes classifier.”
A Survey of Collaborative Filtering Techniques (2009, methodology): “In Miyahara and Pazzani [10], multiclass data are firstly converted to binary-class data, and then converted to a Boolean feature vector rating matrix.”
Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks (2010, methodology): “This kind of model ranges from the classical Naive–Bayes [16,20,21] to the use of more sophisticated techniques such as those based on aspect models [22–25].”
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation (2016, methodology): “There have been a variety of such algorithms including Bayesian methods [9, 10], Restricted Boltzmann Machines [11], Matrix Factorization (MF) methods (the basis of many state-of-the-art recommendation approaches such as [12], [13], [14]), and so on.”
On the Relationship Between Feature Selection and Classification Accuracy (2008, methodology): “Besides some statistical filtering methods like Fisher score (Furey et al., 2000) or Pearson correlation (Miyahara and Pazzani, 2000), information gain, originally used to compute splitting criteria for decision trees, is often used to find out how well each single feature separates the given data…”
Recommender Systems (2016, neutral): “A method for performing collaborative filtering with the use of the Bayes method is discussed in [437].”
Fast Binary Feature Selection with Conditional Mutual Information (2004, methodology): “…features according to their individual predictive power, which can be estimated by various means such as Fisher score (Furey et al., 2000), Kolmogorov-Smirnov test, Pearson correlation (Miyahara and Pazzani, 2000) or mutual information (Battiti, 1994; Bonnlander and Weigend, 1996; Torkkola, 2003).”