  The explosion of cloud services on the Internet brings new challenges in
service discovery and selection. Particularly, the demand for efficient
quality-of-service (QoS) evaluation is becoming urgently strong. To address
this issue, this paper proposes neighborhood-based approach for QoS prediction
of cloud services by taking advantages of collaborative intelligence. Different
from heuristic collaborative filtering and matrix factorization, we define a
formal neighborhood-based prediction framework which allows an efficient global
optimization scheme, and then exploit different baseline estimate component to
improve predictive performance. To validate the proposed methods, a large-scale
QoS-specific dataset which consists of invocation records from 339 service
users on 5,825 web services on a world-scale distributed network is used.
Experimental results demonstrate that the learned neighborhood-based models can
overcome existing difficulties of heuristic collaborative filtering methods and
achieve superior performance than state-of-the-art prediction methods.
