NDLI: Recommending Messages to Users in Social Networks: A Cross-Site Study

Content Provider	IEEE Xplore Digital Library
Author	Cohen, R. Sardana, N. Rahim, K. Lam, D.Y. Li, M. Maccarthy, O. Woo, E. Zhang, J. Guo, G.
Copyright Year	2013
Description	Author affiliation: Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore (Zhang, J.; Guo, G.) \|\| Cheriton Sch. of Comput. Sci., Univ. of Waterloo, Waterloo, ON, Canada (Cohen, R.; Sardana, N.; Rahim, K.; Lam, D.Y.; Li, M.; Maccarthy, O.; Woo, E.)
Abstract	In this paper we produce an algorithm for presenting messages to users in social networks that integrates reasoning about the message, the author, the recipient and the social network. Our proposed model was derived on the basis of immersion within three different existing social networking environments, that of Courser a, Reddit, and medical self-help groups such as PatientsLikeMe. We first present three models, each of which is designed to perform well within the context of one specific social network. From here we derive a generalized model which can be effective regardless of social network context. We conclude with a discussion of possible directions for future research, with an emphasis on promoting the use of trust modeling and user modeling, in a view to exploring additional networks and include as well a comparison to competing models within the artificial intelligence literature. Our aim is to offer insights into coping with the massive amount of information that currently resides within our social networks.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	445
Ending Page	450
File Size	367234
Page Count	6
File Format	PDF
ISBN	9780769551449
DOI	10.1109/ICMLA.2013.160
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Social Networks Social network services Trust Modeling Communities Recommending Messages Medical services Reliability Proposals Message systems
Content Type	Text
Resource Type	Article

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