User Recommendations in Reciprocal and Bipartite Social Networks-An Online Dating Case Study

被引：28

作者：

Zhao, Kang ^{[1
]}

Wang, Xi ^{[2
]}

Yu, Mo ^{[3
]}

Gao, Bo ^{[4
]}

机构：

[1] Univ Iowa, Dept Management Sci, Iowa City, IA 52242 USA

[2] Univ Iowa, Interdisciplinary Grad Program Informat, Iowa City, IA 52242 USA

[3] Penn State Univ, Big Data Social Sci Integrat Grad Educ & Res Trai, University Pk, PA 16802 USA

[4] Beijing Jiaotong Univ, Network Ctr, Beijing, Peoples R China

来源：

IEEE INTELLIGENT SYSTEMS | 2014年 / 29卷 / 02期

基金：

美国国家科学基金会;

关键词：

Intelligent systems; Recommender systems; Collaboration; Facebook; LinkedIn; intelligent systems; reciprocal social network; bipartite; recommendation; link prediction; online dating;

D O I：

10.1109/MIS.2013.104

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Many social networks in our daily life are bipartite networks built on reciprocity. How can we make recommendations to others so that the user is interested in and attractive to those other users whom we've recommended? We propose a new collaborative-filtering model to improve user recommendations in bipartite and reciprocal social networks. The model considers a user's taste in picking others and attractiveness in being picked by others. A case study of an online dating network shows that the approach offers good performance in recommending both initial and reciprocal contacts. © 2001-2011 IEEE.

引用

页码：27 / 35

页数：9