Application of K-means Algorithm in Latent Factor Model
FAN Yuqiang
LONG Huiyun
WU Yun
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 .
Keywords：latent factor modelK-means algorithmrating matrixK-LFM
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 572-574,609 )