协同过滤推荐算法对比分析与优化应用

Collaborative Filtering Recommendation Algorithm Analysis and Optimization Applications

协同过滤推荐算法分为基于内存和基于模型的推荐算法,协同过滤推荐算法存在数据稀疏性、可扩展性、冷启动等问题.通过基于用户、基于项目协同过滤推荐算法以及SVD、Slope-One、KNN等基于模型协同过滤推荐算法对比分析.提出加入特征向量维度优化的SVD算法,通过降维改善数据稀疏性问题.利用Hadoop分布式平台改善推荐算法可扩展性问题.基于Movie Lens数据集实验结果表明,引入基于Boolean相似性计算方法的推荐效果更优,引入数量权重和标准差权重的优化Slope-One算法和引入特征向量维度的优化SVD算法推荐效果更优.

The collaborative filtering recommendation algorithm is divided into user-based and item-based recommendation algorithms. Collaborative filtering recommendation algorithm had data-sparseness and scalability and cold-start problems. This paper mainly studied the collaborative filtering recommendation algorithm based on the users or Items and SVD, Slope-One, KNN. The optimization of SVD algorithm which considers the dimension of the feature space used dimension reduction to improve data-sparseness problem. Using the Hadoop distribution platform to improve the scalability problem. Experimental result shows that the similarity computation method based on Boolean data has better result and the optimization of Slope-One and SVD algorithm have better recommendation result based on MovieLens data set.