Future Generation Computer Systems 82 (2018) 669–678
Contents lists available at ScienceDirect
Future Generation Computer Systems
journal homepage: www.elsevier.com/locate/fgcs
Collaborative QoS prediction with context-sensitive matrix
factorization
Hao Wu
a
, Kun Yue
a
, Bo Li
a
, Binbin Zhang
a
, Ching-Hsien Hsu
b,c,
*
a
School of Information Science and Engineering, Yunnan University, No. 2 North Green Lake Road, Kunming 650091, China
b
School of Mathematics and Big Data, Foshan University, China
c
Feng Chia University, Taichung, Taiwan
h i g h l i g h t s
• This paper presents a general context-sensitive approach for collaborative QoS prediction.
• Interactions of users-to-services and environment-to-environment are considered simultaneously as contextual factors in the QoS data.
• Our method takes advantages of both implicit and explicit factors entailed in the QoS data through exploiting contextual information.
• Experimental results reflect that this study offers an efficient global optimization, enabling robust and accurate prediction results.
a r t i c l e i n f o
Article history:
Received 22 March 2017
Received in revised form 11 May 2017
Accepted 16 June 2017
Available online 24 July 2017
Keywords:
Cloud services
QoS prediction
Context-sensitive
Matrix factorization
a b s t r a c t
How to obtain personalized quality of cloud/IoT services and assist users selecting the appropriate service
has become a hot issue with the explosion of services on the Internet. Collaborative QoS prediction is
proposed to address this issue by borrowing ideas from recommender systems. However, there is still a
challenging problem as how to incorporate contextual factors into existing algorithms to realize context-
aware QoS prediction as contextual factors play a crucial role in QoS assessment. In this paper, we propose
a general context-sensitive matrix-factorization approach (CSMF) to make collaborative QoS prediction.
By considering the complexity of service invocations, CSMF models the interactions of users-to-services
and environment-to-environment simultaneously, and make full use of implicit and explicit contextual
factors in the QoS data. Experimental results show that CSMF significantly outperforms the-state-of-art
methods in metric of prediction accuracy. Particularly, when the QoS data is very sparse, CSMF is more
effective and robust.
© 2017 Elsevier B.V. All rights reserved.
1. Introduction
With the rapid development and widespread deployment of
cloud computing and Internet of Things (IoT) technologies, more
and more homogeneous services emerge on the Internet [1]. Spe-
cially, the amount of cloud-based IoT services will probably ex-
plode with the integration of Cloud, IoT and Service-Oriented Ar-
chitecture [2]. QoS-based service evaluation has become more im-
portant [3,4], as users can take a decision on choosing appropriate
services with distinguishable quality values of candidate services.
Recently, researchers have proposed collaborative QoS prediction
approaches to solve this problem by drawing lessons from the rec-
ommender systems [5–7]. Assume there exist some cyber–physical
*
Corresponding author at: Feng Chia University, Taichung, Taiwan.
E-mail addresses: haowu@ynu.edu.cn (H. Wu), kyue@ynu.edu.cn (K. Yue),
libo@ynu.edu.cn (B. Li), zhangbinbin@gmail.com (B. Zhang), chh@chu.edu.tw
(C. Hsu).
systems (which are being linked to versatile individuals in physical
space and social space [8]) in which the QoS data, user behavioral
data and various contextual information can be collected. Based
on the principle that similar users (or services) tend to observe
similar quality scores on the same service (or user), collaborative
QoS forecasting models are built to predict the unknown quality
values given active users and services as well as associated contexts
(Fig. 1 shows an example in which the observed QoS values are
used to predict the unknown ones). This treatment exploits crowd
intelligence to aid the QoS assessment and avoid intuitive data
measurement, thereby save time and economic costs for both
service providers and users [9]. Consequently, collaborative QoS
prediction has become an increasingly active and highly problem-
rich research area, and many efforts have been done based on
either neighborhood-based collaborative filtering (CF) or matrix-
factorization [9].
Nevertheless, in most of the existing works, only the feedback
matrix which contains explicit quality records observed by users
http://dx.doi.org/10.1016/j.future.2017.06.020
0167-739X/© 2017 Elsevier B.V. All rights reserved.
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