A Recommendation Algorithm on Fusion Latent Factor Model and K-meansplus Clustering Model
QIAO Pingan
CAO Yu
REN Zeqian
Abstract：The traditional recommendation model has many limitations,and its main problems exist in the sparseness and ex?pansibility of the data,resulting in the result of the prediction score is not accurate enough and the calculation efficiency is not high enough.Aiming at the above problems,this paper proposes a fusion algorithm,which combines the latent factor model and the K-meansplus clustering model to solve the problem of sparseness and expansibility.The methods are tested on the MovieLens data, and the results are better than those perviously published on that dataset.
Keywords：latent factor modelK-meansplus cluster modelfusion algorithm
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 1108-1111 )