摘要： 针对协同过滤推荐系统中普遍存在的评分数据稀疏问题,提出一种基于正反相似度的协同过滤推荐算法。通过改进杰卡德相似度模型,给出一种计算用户正反相似度的方法,进而筛选出正反相似用户群,并根据正反相似用户群的已知评分进行综合预测。实验结果证明,与基于相似用户群的协同过滤推荐算法相比,该算法可以有效缓解协同过滤推荐中的数据稀疏问题,并且提高了推荐系统的预测准确率。

关键词: 推荐系统, 协同过滤, 数据稀疏, 正反相似度, 惩罚因子

Abstract: The sparsity of rating data is a common problem in collaborative filtering recommendation systems.This paper proposes a collaborative filtering recommendation algorithm based on positive and negative similarities which presents a calculation method of the positive and negative similarities between users by improving the Jaccard similarity model.Thus,user groups based on positive and negative similarities can be picked out for predicting ratings according to the known score of the user group.Experimental results show that the algorithm can achieve better prediction accuracy for recommendation than collaborative filtering recommendation algorithms based on similar user group.Furthermore,the algorithm can alleviate the dataset sparsity problem.

Key words: recommendation system, collaborative filtering, data sparseness, positive and negative similarities, penalty factor

中图分类号: 

• TP391