Book chapter
Recommendation in Reciprocal and Bipartite Social Networks–A Case Study of Online Dating
Social Computing, Behavioral-Cultural Modeling and Prediction, pp.231-239
Lecture Notes in Computer Science, Springer Berlin Heidelberg
2013
DOI: 10.1007/978-3-642-37210-0_25
Abstract
Many social networks in our daily life are bipartite networks that are built on reciprocity. How can we recommend users/friends to a user, so that the user is interested in and attractive to recommended users? In this research, we propose a new collaborative filtering model to improve user recommendations in reciprocal and bipartite 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 new model outperforms a baseline collaborative filtering model on recommending both initial contacts and reciprocal contacts.
Details
- Title: Subtitle
- Recommendation in Reciprocal and Bipartite Social Networks–A Case Study of Online Dating
- Creators
- Mo Yu - Pennsylvania State UniversityKang Zhao - University of IowaJohn Yen - Pennsylvania State UniversityDerek Kreager - Pennsylvania State University
- Resource Type
- Book chapter
- Publication Details
- Social Computing, Behavioral-Cultural Modeling and Prediction, pp.231-239
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-642-37210-0_25
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 2013
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380435602771
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