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Reciprocal and heterogeneous link prediction in social networks

Citation

Cai, X and Bain, M and Krzywicki, A and Wobcke, W and Kim, YS and Compton, P and Mahidadia, A, Reciprocal and heterogeneous link prediction in social networks, Lecture Notes in Artificial Intelligence 7302: PAKDD 2012, 29 May - 1 June 2012, Kuala Lumpur, Malaysia, pp. 193-204. ISBN 978-3-642-30219-0 (2012) [Refereed Conference Paper]

Copyright Statement

Copyright 2012 Springer

DOI: doi:10.1007/978-3-642-30220-6_17

Abstract

Link prediction is a key technique in many applications in social networks, where potential links between entities need to be predicted. Conventional link prediction techniques deal with either homogeneous entities, e.g., people to people, item to item links, or non-reciprocal relationships, e.g., people to item links. However, a challenging problem in link prediction is that of heterogeneous and reciprocal link prediction, such as accurate prediction of matches on an online dating site, jobs or workers on employment websites, where the links are reciprocally determined by both entities that heterogeneously belong to disjoint groups. The nature and causes of interactions in these domains makes heterogeneous and reciprocal link prediction significantly different from the conventional version of the problem. In this work, we address these issues by proposing a novel learnable framework called ReHeLP, which learns heterogeneous and reciprocal knowledge from collaborative information and demonstrate its impact on link prediction. Evaluation on a large commercial online dating dataset shows the success of the proposed method and its promise for link prediction.

Item Details

Item Type:Refereed Conference Paper
Keywords:recommender systems, social network analysis, machine learning, data mining, information retrieval
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Artificial Intelligence and Image Processing not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Application Software Packages (excl. Computer Games)
Author:Kim, YS (Dr Yang Kim)
ID Code:94661
Year Published:2012
Deposited By:Computing and Information Systems
Deposited On:2014-09-15
Last Modified:2014-12-09
Downloads:0

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