  With the proliferation of Internet-of-Things and continuous growth in the
number of web services at the Internet-scale, the service recommendation is
becoming a challenge nowadays. One of the prime aspects influencing the service
recommendation is the Quality-of-Service (QoS) parameter, which depicts the
performance of a web service. In general, the service provider furnishes the
value of the QoS parameters during service deployment. However, in reality, the
QoS values of service vary across different users, time, locations, etc.
Therefore, estimating the QoS value of service before its execution is an
important task, and thus the QoS prediction has gained significant research
attention. Multiple approaches are available in the literature for predicting
service QoS. However, these approaches are yet to reach the desired accuracy
level. In this paper, we study the QoS prediction problem across different
users, and propose a novel solution by taking into account the contextual
information of both services and users. Our proposal includes two key steps:
(a) hybrid filtering and (b) hierarchical prediction mechanism. On the one
hand, the hybrid filtering method aims to obtain a set of similar users and
services, given a target user and a service. On the other hand, the goal of the
hierarchical prediction mechanism is to estimate the QoS value accurately by
leveraging hierarchical neural-regression. We evaluate our framework on the
publicly available WS-DREAM datasets. The experimental results show the
outperformance of our framework over the major state-of-the-art approaches.
