TY - JOUR
T1 - A hybrid service recommendation prototype adapted for the UCWW
T2 - A smart-city orientation
AU - Zhang, Haiyang
AU - Ganchev, Ivan
AU - Nikolov, Nikola S.
AU - Ji, Zhanlin
AU - O'droma, Máirtín
N1 - Publisher Copyright:
© 2017 Haiyang Zhang et al.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85042540591&partnerID=8YFLogxK
U2 - 10.1155/2017/6783240
DO - 10.1155/2017/6783240
M3 - Article
AN - SCOPUS:85042540591
SN - 1530-8669
VL - 2017
JO - Wireless Communications and Mobile Computing
JF - Wireless Communications and Mobile Computing
M1 - 6783240
ER -