半监督偏好学习算法

doi:10.16451/j.cnki.issn1003-6059.201910005

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Abstract

To improve low robustness and reconstruction accuracy of recommendation system, a semi-supervised preference learning algorithm is proposed to obtain potential preferences via preference learning and implement recommendations. The l_2,1 norm is utilized as the regularization of the optimization objective function to eliminate the noises and outliers. The graph Laplacian regularization is employed to integrate the side information of UI matrix to realize multi-image fusion and improve recommendation precision. The experiments on Movielens 10M and Netflix datasets indicate that the proposed algorithm produces high precision, speed and robustness.

Key words： Semi-supervision Preference Learning Robustness Laplace Regularization l_2,1 Norm

Received: 17 May 2019

ZTFLH:

TP 18

Fund:

Supported by National Natural Science Foundation of China(No.61572419,61773331,61703360)

Corresponding Authors: LIU Jinglei, Ph.D., professor. His research interests include artificial intelligent and theoretical computer science.

About author:: ZHAO Min, master student. Her research interests include robustness of semi-supervised preference learning.

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Cite this article:

ZHAO Min,LIU Jinglei. Semi-supervised Preference Learning Algorithm[J]. , 2019, 32(10): 909-916.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201910005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I10/909

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