People-to-People recommendation using multiple compatible subgroups

Authors:
Yang Sok Kim;Ashesh Mahidadia;Paul Compton;Alfred Krzywicki;Wayne Wobcke;Xiongcai Cai;Michael Bain
Affiliations:
School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
Venue:
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Year:
2012

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Abstract

People-to-people recommendation aims at suggesting suitable matches to people in a way that increases the likelihood of a positive interaction. This problem is more difficult than conventional item-to-people recommendation since the preferences of both parties need to be taken into account. Previously we proposed a profile-based recommendation method that first uses compatible subgroup rules to select a single best attribute value for each corresponding value of the user, then combines these attribute value pairs into a rule that determines the recommendations. Though this method produces a significant improvement in the probability of an interaction being successful, it has two significant limitations: (i) by considering only single matching attribute values the method ignores cases where different attribute values are closely related, missing potential candidates, and (ii) when ranking candidates for recommendation the method does not consider individual behaviour. This paper addresses these two issues, showing how multiple attributes can be used with compatible subgroup rules and individual reply rates used for ranking candidates. Our experimental results show that the new approach significantly improves the probability of an interaction being successful compared to our previous approach.