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ProCF: probabilistic collaborative filtering for reciprocal recommendation
Citation
Cai, X and Bain, M and Krzywicki, A and Wobcke, W and Kim, YS and Compton, P and Mahidadia, A, ProCF: probabilistic collaborative filtering for reciprocal recommendation, Lecture Notes in Artificial Intelligence 7819: PAKDD 2013, 14-17 April 2013, Gold Coast, Australia, pp. 1-12. ISBN 978-3-642-37455-5 (2013) [Refereed Conference Paper]
Copyright Statement
Copyright 2013 Springer
DOI: doi:10.1007/978-3-642-37456-2_1
Abstract
Similarity in people to people (P2P) recommendation in
social networks is not symmetric, where both entities of a relationship
are involved in the reciprocal process of determining the success of the
relationship. The widely used memory-based collaborative filtering (CF)
has advantages of effectiveness and efficiency in traditional item to people
recommendation. However, the critical step of computation of similarity
between the subjects or objects of recommendation in memory-based
CF is typically based on a heuristically symmetric relationship, which
may be flawed in P2P recommendation. In this paper, we show that
memory-based CF can be significantly improved by using a novel asymmetric
model of similarity that considers the probabilities of both positive
and negative behaviours, for example, in accepting or rejecting a
recommended relationship. We present also a unified model of the fundamental
principles of collaborative recommender systems that subsumes
both user-based and item-based CF. Our experiments evaluate the proposed
approach in P2P recommendation in the real world online dating
application, showing significantly improved performance over traditional
memory-based methods.
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: | 94670 |
Year Published: | 2013 |
Deposited By: | Information and Communication Technology |
Deposited On: | 2014-09-15 |
Last Modified: | 2014-12-09 |
Downloads: | 0 |
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