基于概念格的稀疏数据协同过滤校正自然噪声方法

针对推荐系统中存在的自然噪声问题, 提出一种基于概念格的稀疏数据协同过滤校正自然噪声的方法. 首先将用户和项目划分为强、 平均和弱三类检测自然噪声, 然后采用基于概念格的稀疏数据协同过滤校正这些自然噪声, 最后从获得的无自然噪声数据集中预测未评级的项目. 在含自然噪声的数据集上进行实验的结果表明, 该方法具有较高的推荐精度, 且在数据稀疏的情形下仍具有良好的性能.

Collaborative Filtering Correction of Natural Noise Method of Sparse Data Based on Concept Lattice

Aiming at the problem of natural noise in the recommended system, we proposed a collaborative filtering correction of natural noise method of sparse data based on concept lattice. Firstly, users and items were divided into three classes: strong, average and weak to detect natural noise. Secondly, the collaborative filtering of sparse data based on concept lattice was used to correct these natural noises. Finally, the unrated items were predicted from the obtained dataset without natural noise. The experimental results on the dataset containing natural noise show that the proposed method has high recommendation accuracy and good performance in the case of sparse data.