基于相似度优化和流形学习的协同过滤算法改进研究

协同过滤算法中存在着数据稀疏性和可扩展性问题,由于用户和项目数据量巨大致使数据十分稀疏,且不同数据集中数据存在差异,致使现有算法中的相似度计算不够准确和用户聚类效果不佳,对推荐算法准确率产生了显著影响。为了提高相似度计算和最近邻居搜索的准确率,提出了一种基于相似度优化和流形学习的协同过滤算法。通过加权因子优化相似度计算,结合流形学习对稀疏的用户评分数降维后进行谱聚类,通过获得的全局最优解提高聚类所得目标用户最近邻居的准确率,进而提高协同过滤推荐精度。在Epinions数据集和MovieLens数据集上进行实验,结果表明,提出的算法可以有效降低协同过滤算法的平均绝对误差和均方根误差,提高召回率,拥有更高的推荐准确率。

An improved collaborative filtering algorithm based on similarity optimization and manifold learning

There are data sparsity and scalability problems in collaborative filtering algorithms. Due to the huge amount of user and project data, the data is very sparse, and the data under different data sets are different, which makes the similarity calculation and user clustering effect in the existing algorithms not accurate enough and has a significant impact on the accuracy of the recommendation algorithms. In order to improve the accuracy of similarity calculation and nearest neighbor search, this paper proposes a collaborative filtering algorithm based on similarity optimization and manifold learning. By using the weighted factor to calculate the optimization similarity and combining the manifold learning, spectral clustering is carried out on sparse users after their scores are evaluated and the dimensionality is reduced. The obtained global optimal solution can improve the accuracy of the nearest neighbor of the target users in clustering, thus improving the recommendation accuracy of collaborative filtering. Experimental results on Epinions databset and Movielens databset show that the proposed algorithm can effectively reduce the mean absolute error and root mean square error of collaborative filtering algorithms, improve the recall rate and achieve higher recommendation accuracy.