摘要
为了克服协同推荐系统中的用户评分数据稀疏性和推荐实时性差的问题,提出了一种高效的基于粗集的个性化推荐算法.该算法首先利用维数简化技术对评分矩阵进行优化,然后采用分类近似质量计算用户间的相似性形成最近邻居,从而降低数据稀疏性和提高最近邻寻找准确性.实验结果表明,该算法有效地解决用户评分数据极端稀疏情况下传统相似性度量方法存在的问题,显著地提高推荐系统的推荐质量.
In order to overcome the problem of traditional collaborative filtering algorithm faces severe challenge of sparse user ratings and real——time recommendation,a high efficient personalization recommendation algorithm based on rough set is proposed.The algorithm refine the user ratings data using dimensionality reduction,then uses the quality of approximation of classification to find the target users＇neighbors.Thus the sparsity can be decreased and the accuracy of searching nearest neighbor can be improved.The experimental results show that this method can efficiently improve the extreme sparsity of user rating data,and provide better recommendation results than traditional collaborative filtering algorithms.
出处
《微电子学与计算机》
CSCD
北大核心
2012年第3期69-72,77,共5页
Microelectronics & Computer
基金
江苏省自然科学基金项目(BK2008190)
关键词
推荐系统
最近邻
用户相似性
维数简化
recommendation system
nearest neighbor
user similarity
dimensionality reduction
