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Using a critic to promote less popular candidates in a people-to-people recommender system
conference contribution
posted on 2023-05-23, 09:20 authored by Krzywicki, A, Wobcke, W, Cai, X, Bain, M, Mahidadia, A, Compton, P, Kim, YSThis 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.
History
Publication title
Proceedings of the 24th Innovative Applications of Artificial Intelligence Conference 2012Editors
M Fromherz and H Munoz-AvilaPagination
2305-2310ISBN
978-1-57735-568-7Department/School
School of Information and Communication TechnologyPublisher
AAAIPlace of publication
Palo Alto, CaliforniaEvent title
24th Innovative Applications of Artificial Intelligence Conference 2012Event Venue
Toronto, CanadaDate of Event (Start Date)
2012-07-22Date of Event (End Date)
2012-07-26Rights statement
Copyright 2012 AAAIRepository Status
- Restricted