Title: Modelling geographical effect of user neighbourhood on collaborative web service QoS prediction

Authors: Zhen Chen; Limin Shen; Dianlong You; Huihui Jia; Chuan Ma; Feng Li

Addresses: College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China ' College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China ' College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China ' College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China ' College of Information Science and Engineering, Yanshan University, Qinhuangdao, 066004, China ' College of Computer Science and Engineering, Northeastern University, Shenyang, 110000, China

Abstract: QoS prediction is a task to predict the unknown QoS value of an active user to a web service that he/she has not accessed previously for supporting appropriate web service recommendation. Existing studies adopt collaborative filtering methods for QoS prediction, while the inherent issues of data sparsity and cold-start in collaborative filtering have not been resolved satisfactorily and the role of geographical context is also underestimated. Through data analysis on a public real-world dataset, we observe that there exists a positive correlation between a user's QoS values and geographical neighbourhood's ratings. Based on the observation, we model the geographical effect of user neighbourhood on QoS prediction and propose a unified matrix factorisation model by capitalising the advantages of geographical neighbourhood and latent factor approaches. Experimental results exhibit the significance of geographical context on modelling user features and demonstrate the feasibility and effectiveness of our approach on improving QoS prediction performance.

Keywords: web service; QoS prediction; collaborative filtering; geographical effect; matrix factorisation.

DOI: 10.1504/IJHPCN.2019.101258

International Journal of High Performance Computing and Networking, 2019 Vol.14 No.2, pp.208 - 218

Received: 27 Dec 2016
Accepted: 27 Jun 2017
Published online: 30 Jul 2019 *

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