Journal article
User Recommendations in Reciprocal and Bipartite Social Networks-An Online Dating Case Study
IEEE intelligent systems, Vol.29(2), pp.27-35
03/01/2014
DOI: 10.1109/MIS.2013.104
Abstract
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.
Details
- Title: Subtitle
- User Recommendations in Reciprocal and Bipartite Social Networks-An Online Dating Case Study
- Creators
- Kang Zhao - Univ Iowa, Dept Management Sci, Iowa City, IA 52242 USAXi Wang - Univ Iowa, Interdisciplinary Grad Program Informat, Iowa City, IA 52242 USAMo Yu - Pennsylvania State UniversityBo Gao - Beijing Jiaotong University
- Resource Type
- Journal article
- Publication Details
- IEEE intelligent systems, Vol.29(2), pp.27-35
- DOI
- 10.1109/MIS.2013.104
- ISSN
- 1541-1672
- eISSN
- 1941-1294
- Publisher
- IEEE
- Number of pages
- 9
- Grant note
- Pennsylvania State University DEG-1144860 / US National Science Foundation; National Science Foundation (NSF) University of Iowa Old Gold Summer Fellowship
- Language
- English
- Date published
- 03/01/2014
- Academic Unit
- Center for Evaluation and Assessment; Business Analytics
- Record Identifier
- 9984380506702771
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