基于信念网算法的多类数据协同过滤

Xiaoyuan Su, T. Khoshgoftaar
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引用次数: 125

摘要

作为最成功的推荐系统之一,协同过滤算法可以应对高稀疏性和高可扩展性等挑战。贝叶斯信念网(BNs)是最常用的分类器之一,可用于CF任务。以前将bp应用于CF任务的工作主要集中在二类数据上,并使用简单或基本的贝叶斯分类器(Miyahara和Pazzani, 2002;Breese et al., 1998)。在这项工作中,我们将先进的神经网络模型应用于CF任务,而不是简单的任务,并处理现实世界的多类CF数据,而不是合成的二元类数据。实证结果表明,基于朴素贝叶斯和树增强朴素贝叶斯(NB-ELR和TAN-ELR)模型(Greiner等人,2005)的扩展逻辑回归处理不完整数据的能力始终优于最先进的基于Pearson相关的CF算法。此外,elr优化的BNs CF模型在预测能力方面具有鲁棒性,而基于Pearson相关性的CF算法的鲁棒性随着数据稀疏度的增加而下降
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Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms
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 (Miyahara and Pazzani, 2002; Breese et al., 1998). 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 naive Bayes and tree augmented naive Bayes (NB-ELR and TAN-ELR) models (Greiner et al., 2005) 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
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