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Wireless Communications and Mobile Computing
Volume 2017, Article ID 6783240, 11 pages
Research Article

A Hybrid Service Recommendation Prototype Adapted for the UCWW: A Smart-City Orientation

1Telecommunications Research Centre (TRC), University of Limerick, Limerick, Ireland
2Department of Computer Systems, University of Plovdiv “Paisii Hilendarski”, Plovdiv, Bulgaria
3Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland
4North China University of Science and Technology, Tangshan, China

Correspondence should be addressed to Ivan Ganchev;

Received 1 April 2017; Revised 11 August 2017; Accepted 20 August 2017; Published 12 October 2017

Academic Editor: Damianos Gavalas

Copyright © 2017 Haiyang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the development of ubiquitous computing, recommendation systems have become essential tools in assisting users in discovering services they would find interesting. This process is highly dynamic with an increasing number of services, distributed over networks, bringing the problems of cold start and sparsity for service recommendation to a new level. To alleviate these problems, this paper proposes a hybrid service recommendation prototype utilizing user and item side information, which naturally constitute a heterogeneous information network (HIN) for use in the emerging ubiquitous consumer wireless world (UCWW) wireless communication environment that offers a consumer-centric and network-independent service operation model and allows the accomplishment of a broad range of smart-city scenarios, aiming at providing consumers with the “best” service instances that match their dynamic, contextualized, and personalized requirements and expectations. A layered architecture for the proposed prototype is described. Two recommendation models defined at both global and personalized level are proposed, with model learning based on the Bayesian Personalized Ranking (BPR). A subset of the Yelp dataset is utilized to simulate UCWW data and evaluate the proposed models. Empirical studies show that the proposed recommendation models outperform several widely deployed recommendation approaches.