Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms
- Xiaoyuan Su
- Taghi Khoshgoftaar, Computer Science and Engineering, Florida Atlantic University, Boca Raton FL 33431, USA
As one of the most successful recommender systems, collaborative filtering (CF) algorithms can deal with high sparsity and high requirement of scalability amongst other challenges. Bayesian belief nets (BNs), one of the most frequently used classifiers, can be used for CF tasks. Previous works of applying BNs to CF tasks were mainly focused on binary-class data, and used simple or basic Bayesian classifiers [1][2]. In this work, we apply advanced BNs models to CF tasks instead of simple ones, and work on real-world multi-class CF data instead of synthetic binary-class data. Empirical results show that with their ability to deal with incomplete data, extended logistic regression on naïve Bayes and tree augmented naïve Bayes (NB-ELR and TAN-ELR) models [3] consistently perform better than the state-of-the-art Pearson correlation-based CF algorithm. In addition, the ELR-optimized BNs CF models are robust in terms of the ability to make predictions, while the robustness of the Pearson correlation-based CF algorithm degrades as the sparseness of the data increases.
Citation
X. Su, T. Khoshgoftaar. "Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms". Fifteenth IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp 497-504, November 2006.| Keywords: | collaborative filtering, incomplete data, Bayesian networks |
| Category: | In Conference |
BibTeX
@incollection{Su+Khoshgoftaar:ICTAI06,
author = {Xiaoyuan Su and Taghi Khoshgoftaar},
title = {Collaborative Filtering for Multi-class Data Using Belief Nets
Algorithms},
Pages = {497-504},
booktitle = {Fifteenth IEEE International Conference on Tools with Artificial
Intelligence (ICTAI)},
year = 2006,
}Submitted by Xiaoyuan Su
