Recommending access points to individual mobile users via automatic group learning

Abstract

© 2017 IEEE. We consider user to cell association in a heterogeneous network with a mix of LTE/3G and WiFi cells. Individual user preferences are often neglected when a user to cell association decision is made. In this paper we propose use of a recommender system to inform the mapping of users to cells. We demonstrate the effectiveness of the proposed grouped-based user to cell associations for a set of synthetically generated user/cell ratings.

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Item Type:	Proceedings Paper
Authors/Creators:	Partov, B. Leith, D.J. Checco, A. https://orcid.org/0000-0002-0981-3409
Copyright, Publisher and Additional Information:	© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Dates:	Published: 31 July 2017 Published (online): 31 July 2017
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)
Depositing User:	Symplectic Sheffield
Date Deposited:	19 Jan 2018 15:57
Last Modified:	19 Jan 2018 16:05
Published Version:	https://doi.org/10.1109/ICC.2017.7997424
Status:	Published
Publisher:	IEEE
Refereed:	Yes
Identification Number:	10.1109/ICC.2017.7997424
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:126298

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Recommending access points to individual mobile users via automatic group learning

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