Journal
INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
Volume 26, Issue 4, Pages 611-632Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0218194016400040
Keywords
Web service; QoS prediction; network location; matrix factorization
Categories
Funding
- Zhejiang Provincial Natural Science Foundation [LY12F 02003]
- China Postdoctoral Science Foundation [2013M540492]
- National Key Technology RD Program [2014BAK14B04]
Ask authors/readers for more resources
Web service recommendation is one of the key problems in service computing, especially in the case of a large number of service candidates. The QoS (quality of service) values are usually leveraged to recommend services that best satisfy a user's demand. There are many existing methods using collaborative filtering (CF) to predict QoS missing values, but very limited works can leverage the network location information in the user side and service side. In real-world service invocation scenario, the network location of a user or a service makes great impact on QoS. In this paper, we propose a novel collaborative recommendation framework containing three novel prediction models, which are based on two techniques, i.e. matrix factorization (MF) and network location-aware neighbor selection. We first propose two individual models that have the capability of using the user and service information, respectively. Then we propose a unified model that combines the results of the two individual models. We conduct sufficient experiments on a real-world dataset. The experimental results demonstrate that our models achieve higher prediction accuracy than baseline models, and are not sensitive to the parameters.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available