基于径向基函数插值与SVM的协同过滤算法

A Collaborative Filtering Algorithm Based on Radial Basis Function Interpolation and SVM

为了解决在推荐系统中由于数据的稀疏性导致协同过滤算法准确率低下的问题,提出一种混合径向基函数插值和SVM分类的协同过滤推荐算法。该方法先利用径向基函数插值方法对训练数据中缺失数据进行预测与填补,然后利用支持向量机方法根据插值后的数据来对未知数据进行预测,从而形成推荐列表为系统提供推荐服务。实验结果表明该方法克服了数据质量对推荐算法的影响,相比其他SVM方法具有更高的准确率和稳定性。

Due to the fact that the data sparseness leads to the low accuracy of a collaborative filtering recommendation system, this paper proposed a collaborative filtering algorithm based on Radial Basis Function （RBF） interpolation and SVM. The algorithm first uses the RBF interpolation to fill the missing data in training data set, and then a SVM classifier is introduced to predict the label for testing data by using the interpolated data set as training set. The test result shows that the method overcomes the impact of data quality on the recommended algorithm, and it outperforms other methods in accuracy and stability.