A new weighting method in network-based recommendation

被引：24

作者：

Jia, Chun-Xiao ^{[1
,2
]}

Liu, Run-Ran ^{[1
,2
]}

Sun, Duo ^{[1
,2
]}

Wang, Bing-Hong ^{[1
,2
,3
]}

机构：

[1] Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Anhui, Peoples R China

[2] Univ Sci & Technol China, Ctr Nonlinear Sci, Hefei 230026, Anhui, Peoples R China

[3] Shanghai Acad Syst Sci, Inst Complex Adapt Syst, Shanghai, Peoples R China

来源：

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS | 2008年 / 387卷 / 23期

基金：

中国国家自然科学基金; 高等学校博士学科点专项科研基金;

关键词：

weighting method; recommendation algorithm; mass diffusion approach; influence-based approach; initial resource configuration;

D O I：

10.1016/j.physa.2008.06.046

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

In this paper, we propose a influence-based approach to investigate network-based recommendation systems. Different from the previous mass diffusion approach, we give a new expression of initial resource distribution and take into account the influence of resources associated with the receiver nodes. According to ranking score and two measures about the degree of personalization, we demonstrate that our method can outperform the previous methods greatly. It's found that there exists an optimal initial resource distribution that leads to the best algorithmic accuracy and personalization strength. The optimal initial resource distribution indicates that we should increase the initial resource located on popular objects, rather than decrease them. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.

引用

页码：5887 / 5891

页数：5

共 13 条

[1] Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions [J].