一种改进的混合协同过滤推荐算法

An Improved Hybrid Collaborative Filtering Recommendation Algorithm

传统协同过滤算法存在着相似度计算差和因数据稀疏而导致推荐信息不准确问题。文中通过改进相似度计算方法,提出新的混合协同过滤算法框架,以提高推荐质量。其中,对相似度计算方法的改进采用加权方式,而新的框架是将基于内存的两种协同过滤算法进行结合,最终得到一种混合协同过滤算法。通过Netflix提供的数据集进行实验,实验结果表明,该算法相比于传统协同过滤算法有更好的效果。

Traditional collaborative filtering algorithms suffer from poor similarity computation and sparse data,which lead to inaccurate recommendation information. In this paper,a new hybrid collaborative filtering algorithm is proposed to improve the quality of recommendation. The method of similarity computing is improved by using the weighted method,and the new framework is based on the memory of the two kinds of collaborative filtering algorithm.Experiment with the data set provided by Netflix shows that the algorithm has better effect than the traditional collaborative filtering algorithm.