Recommendation in reciprocal and bipartite social networks: a case study of online dating

Authors:
Mo Yu;Kang Zhao;John Yen;Derek Kreager
Affiliations:
The Pennsylvania State University, University Park, PA;Tippie College of Business, The University of Iowa, Iowa City, IA;The Pennsylvania State University, University Park, PA;The Pennsylvania State University, University Park, PA
Venue:
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Year:
2013

Citing 5
Cited 0

Predicting positive and negative links in online social networks

Proceedings of the 19th international conference on World wide web
Why Does Collaborative Filtering Work? Transaction-Based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs

INFORMS Journal on Computing
Who will follow you back?: reciprocal relationship prediction

Proceedings of the 20th ACM international conference on Information and knowledge management
Simulating inter-organizational collaboration network: a multi-relational and event-based approach

Simulation
Reciprocal and heterogeneous link prediction in social networks

PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

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.