计算机与数字工程2016，Vol.44Issue(4)：572-574,609,4.DOI:10.3969/j.issn.1672-9722.2016.04.002
K-means 算法在隐语义模型中的应用磁
Application of K-means Algorithm in Latent Factor Model
摘要
Abstract
Latent Factor Model(LFM ) is an important model widely used in text mining .It has the advantage of high precision and low memory cost in rating prediction .However LFM model is not suitable for processing large‐scale sparse ma‐trix .In order to improve the performance ,K‐means algorithm is introduced to deal with rating data into LFM .This new model is called K‐LFM .First of all ,K‐means is used to classify user and item information in K‐LFM .And then the rating matrices are refactored to reduce the scale and sparse degree of orignal matrix .Finally training model with refactoring matix , can get predict rating .The experiment on public data set movielens shows that K‐LFM model is superior to LFM model on processing efficiency .Besides ,the prediction accuracy isn't significantly affected .关键词
隐语义模型/K-means 算法/评分矩阵/K-LFMKey words
latent factor model/K-means algorithm/rating matrix/K-LFM分类
信息技术与安全科学引用本文复制引用
范玉强,龙慧云,吴云..K-means 算法在隐语义模型中的应用磁[J].计算机与数字工程,2016,44(4):572-574,609,4.基金项目
贵州省科学技术基金项目（编号黔科合 J 字［2010］2100号）；贵州大学引进人才科研项目（编号贵大人基合字（2009）029号）资助。 （）
