一种基于用户兴趣的协同过滤算法

摘要

Aiming at the problems of lower recommendation quality caused by rating data sparseness, using user interest information, an improved user-based clustering collaborative filtering algorithm is proposed in this paper, which improves the algorithm by user similarity calculating method and user-item rating matrix extended. By comparing the MAE of the improved algorithm, this algorithm can calculate the similarity of users more accurately, and reduce the impact of data sparseness. The experimental results show that the new algorithm can efficiently improve the predicted accuracy of collaborative filtering algorithm.%针对评分数据矩阵的稀疏性导致推荐算法质量较低的问题,文章设计了一个改进的协同过滤算法:通过在算法中加入了用户兴趣的信息的方法,改进了用户相似度计算和扩展用户评分矩阵,从而改进了基于用户聚类的协同过滤算法.通过对比改进后算法的平均绝对偏差,该算法可以更准确地计算用户的相似性,并且降低数据稀疏性所带来的影响.实验的结果表明,该算法提高了协同过滤算法的准确性.