Using a Critic to Promote Less Popular Candidates in a People-to-People Recommender System

Authors

  • A. Krzywicki University of New South Wales
  • W. Wobcke University of New South Wales
  • X. Cai University of New South Wales
  • M. Bain University of New South Wales
  • A. Mahidadia University of New South Wales
  • P. Compton University of New South Wales
  • Y. S. Kim University of New South Wales

DOI:

https://doi.org/10.1609/aaai.v26i2.18974

Abstract

This paper shows how to improve the recommendations of an interaction-based collaborative filtering (IBCF) recommender used in online dating. Previous work has shown that IBCF works well in this domain, although it tends to rank popular candidates highly, which leads to these users receiving a large number of contacts. We address this problem by using a Decision Tree model as a “critic” to re-rank the candidates generated by IBCF, effectively promoting less popular candidates. This method was first evaluated on historical data from a large online dating site and then trialled live on the same site by providing recommendations to a large number of users throughout a 9 week period. The live trial confirmed the consistency of the analysis on historical data and the ability of the method to generate suitable candidates over an extended period. Our recommendations gave higher success rates than those for a control group made with a baseline recommender.

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Published

2012-07-22

How to Cite

Krzywicki, A., Wobcke, W., Cai, X., Bain, M. ., Mahidadia, A., Compton, P., & Kim, Y. (2012). Using a Critic to Promote Less Popular Candidates in a People-to-People Recommender System. Proceedings of the AAAI Conference on Artificial Intelligence, 26(2), 2305-2310. https://doi.org/10.1609/aaai.v26i2.18974

Issue

Vol. 26 No. 2 (2012): Twenty-Fourth Innovative Applications of Artificial Intelligence Conference

Section

IAAI Technical Track: Emerging Papers