Improvement of Naïve Bayes Collaborative Filtering Using Interval Estimation

Authors:
V. Robles;P. Larrañaga;E. Menasalvas;M. S. Pérez;V. Herves
Affiliations:
-;-;-;-;-
Venue:
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Year:
2003

Citing 0
Cited 6

A Web-Based Intelligent Tutoring System for Computer Programming

WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
A web-based bayesian intelligent tutoring system for computer programming

Web Intelligence and Agent Systems
A probabilistic model for item-based recommender systems

Proceedings of the 2007 ACM conference on Recommender systems
Scalable pseudo-likelihood estimation in hybrid random fields

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
2009 Special Issue: A hybrid random field model for scalable statistical learning

Neural Networks
NB+: An improved Naïve Bayesian algorithm

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recommender systems emerged to help users choose among the large amount of options that e-commerce sites offer. Collaborative filtering is one of the most successful recommender techniques. In this paper we propose an approach to collaborative filtering based on the simple Bayesian classifier. We propose a method of increasing the efficiency of naïve bayes by applying a new semi naïve Bayes approach based on interval estimation. To evaluate our algorithm we use a database of Microsoft Anonymous Web Data from the UCI repository. Our empirical results show that our proposed interval based naïve Bayes approach outperforms typical naïve bayes1.