摘要
协同过滤算法作为推荐系统中应用最广泛的算法之一,在大数据环境下面临严重的数据稀疏问题,使得近邻选择的效果不佳,直接影响了算法的推荐性能.为了解决这一问题,本文提出了一种基于用户部分特征的协同过滤算法(UPCF),该算法首先基于评分偏差和项目流行度进行矩阵缺失值填充,随后利用初始聚类中心优化的K-means算法对该填充矩阵进行项目聚类,并利用用户在项目分类下的局部特征进行近邻集合构建,最终采用基于用户的协同过滤算法获得推荐.我们采用流行的MAE指标对算法在Movie Lens数据集上进行评测.实验表明,与目前流行的协同过滤算法相比,提出的UPCF算法在没有增加算法复杂性的前提下,性能有近10%的提升.
As one of the most widely used algorithms in recommender system, the traditional collaborative filtering algorithm faces serious data sparseness problem in the big data trend, which leads to the ineffective in nearest neighbor selection, and restricts the performance of the algorithm. To address this problem, this paper proposes a collaborative filtering algorithm based on user partial feature（UPCF）. In our method, it first rates the missing values based on rating bias and item popularity; and then clusters the items in the filled matrix with a K-means clustering algorithm of meliorated initial center. At last, it uses the user-based collaborative filtering algorithm with the user feature in item class to get the recommendations. The MAE measures on the Movie Lens dataset shows that compared with the current popular algorithms, the performance of our UPCF algorithm improves about 10% without any increase of algorithm complexity.
作者
李永超
罗军
LI Yong-Chao LUO Jun(Department of Computer Science, National University of Defense Technology, Changsha 410073, China)
出处
《计算机系统应用》
2017年第3期204-208,共5页
Computer Systems & Applications
关键词
项目流行度
最近邻选择
项目聚类
协同过滤算法
item popularity
nearest neighbor selection
item clustering
collaborative filtering algorithm
