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Hybrid techniques to address cold start problems for people to people recommendation in social networks
Citation
Kim, YS and Krzywicki, A and Wobcke, W and Mahidadia, A and Compton, P and Cai, X and Bain, M, Hybrid techniques to address cold start problems for people to people recommendation in social networks, Lecture Notes in Artificial Intelligence 7458: PRICAI 2012, 3-7 September 2012, Kuching, Malaysia, pp. 206-217. ISBN 978-3-642-32694-3 (2012) [Refereed Conference Paper]
Copyright Statement
Copyright 2012 Springer
DOI: doi:10.1007/978-3-642-32695-0_20
Abstract
We investigate several hybrid approaches to suggesting matches in
people to people social recommender systems, paying particular attention to cold
start problems, problems of generating recommendations for new users or users
without successful interactions. In previous work we showed that interaction-based
collaborative filtering (IBCF) works well in this domain, although this approach
cannot generate recommendations for new users, whereas a system based
on rules constructed using subgroup interaction patterns can generate recommendations
for new users, but does not perform as effectively for existing users. We
propose three hybrid recommenders based on user similarity and two content-boosted
recommenders used in conjunction with interaction-based collaborative
filtering, and show experimentally that the best hybrid and content-boosted recommenders
improve on the IBCF method (when considering user success rates)
yet cover almost the whole user base, including new and previously unsuccessful
users, thus addressing cold start problems in this domain. The best content-boosted
method improves user success rates more than the best hybrid method
over various "cold start" subgroups, but is less computationally efficient overall.
Item Details
Item Type: | Refereed Conference Paper |
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Keywords: | recommender systems, social network analysis |
Research Division: | Information and Computing Sciences |
Research Group: | Artificial intelligence |
Research Field: | Artificial intelligence not elsewhere classified |
Objective Division: | Information and Communication Services |
Objective Group: | Information systems, technologies and services |
Objective Field: | Application software packages |
UTAS Author: | Kim, YS (Dr Yang Kim) |
ID Code: | 94667 |
Year Published: | 2012 |
Deposited By: | Information and Communication Technology |
Deposited On: | 2014-09-15 |
Last Modified: | 2014-12-09 |
Downloads: | 0 |
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