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People-to-people recommendation using multiple compatible subgroups
conference contribution
posted on 2023-05-23, 09:20 authored by Kim, YS, Mahidadia, A, Compton, P, Krzywicki, A, Wobcke, W, Cai, X, Bain, MPeople-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.
History
Publication title
Lecture Notes in Computer Science 7691: AI 2012Editors
M Thielscher, D ZhangPagination
61-72ISBN
978-3-642-35100-6Department/School
School of Information and Communication TechnologyPublisher
Springer-VerlagPlace of publication
GermanyEvent title
25th Australasian Joint Conference on Advances in Artificial Intelligence 2012Event Venue
Sydney, AustraliaDate of Event (Start Date)
2012-12-04Date of Event (End Date)
2012-12-07Rights statement
Copyright 2012 SpringerRepository Status
- Restricted