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Title: Collaborative filtering-based recommendation of online social voting
Authors: Yang, X
Liang, C
Zhao, M 
Wang, H
Ding, H
Liu, Y
Li, Y
Zhang, J
Keywords: Collaborative filtering
Online social networks (OSNs)
Recommender systems (RSs)
Social voting
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: IEEE transactions on computational social systems, 2017, v. 4, no. 1, 7866820, p. 1-13 How to cite?
Journal: IEEE transactions on computational social systems 
Abstract: Social voting is an emerging new feature in online social networks. It poses unique challenges and opportunities for recommendation. In this paper, we develop a set of matrix-factorization (MF) and nearest-neighbor (NN)-based recommender systems (RSs) that explore user social network and group affiliation information for social voting recommendation. Through experiments with real social voting traces, we demonstrate that social network and group affiliation information can significantly improve the accuracy of popularity-based voting recommendation, and social network information dominates group affiliation information in NN-based approaches. We also observe that social and group information is much more valuable to cold users than to heavy users. In our experiments, simple metapath-based NN models outperform computation-intensive MF models in hot-voting recommendation, while users' interests for nonhot votings can be better mined by MF models. We further propose a hybrid RS, bagging different single approaches to achieve the best top-k hit rate.
EISSN: 2329-924X
DOI: 10.1109/TCSS.2017.2665122
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