Personalized QoS-Aware Web Service
Recommendation
and Visualization
ABSTRACT:
With the proliferation of web services, effective
QoS-based approach to service recommendation is becoming more and more
important. Although service recommendation has been studied in the recent
literature, the performance of existing ones is not satisfactory, since 1)
previous approaches fail to consider the QoS variance according to users’
locations; and 2) previous recommender systems are all black boxes providing
limited information on the performance of the service candidates. In this
paper, we propose a novel collaborative filtering algorithm designed for
large-scale web service recommendation. Different from previous work, our
approach employs the characteristic of QoS and achieves considerable
improvement on the recommendation accuracy. To help service users better
understand the rationale of the recommendation and remove some of the mystery,
we use a recommendation visualization technique to show how a recommendation is
grouped with other choices. Comprehensive experiments are conducted using more
than 1.5 million QoS records of real-world web service invocations. The
experimental results show the efficiency and effectiveness of our approach.
EXISTING SYSTEM:
The objective of this paper is to make
personalized QoS based web service recommendations for different users and thus
help them select the optimal one among the functional equivalents. Several
previous works has applied collaborative filtering (CF) to web service
recommendation. These CF-based web service recommender systems works by
collecting user observed QoS records of different web services and matching
together users who share the same information needs or same tastes. Users of a
CF system share their judgments and opinions on web services, and in return,
the system provides useful personalized recommendations.
DISADVANTAGES
OF EXISTING SYSTEM:
However, three unsolved problems of the
previous work affect the performance of current service recommender systems:
1.
The first problem is that the existing
approaches fail to recognize the QoS variation with users’ physical locations.
2.
The second problem is the online time complexity
of memory-based CF recommender systems. The increasing number of web services
and users will pose a great challenge to current systems.
3.
The last problem is that current web
service recommender systems are all black boxes, providing a list of ranked web
services with no transparency into the reasoning behind the recommendation
results .It is less likely for users to trust a recommendation when they have
no knowledge of the underlying rationale.
PROPOSED SYSTEM:
In this proposed system
we propose an innovative CF algorithm for QoS-based web service recommendation.
To address the third problem and enable an improved understanding of the web
service recommendation rationale, we provide a personalized map for browsing
the recommendation results. The map explicitly shows the QoS relationships of
the recommended web services as well as the underlying structure of the QoS
space by using map metaphor such as dots, areas, and spatial arrangement.
ADVANTAGES
OF PROPOSED SYSTEM:
The main contributions of this work are
threefold:
n First, we combine the model-based and memory
based CF algorithms for web service recommendation, which significantly
improves the recommendation accuracy and time complexity compared with previous
service recommendation algorithms.
n Second, we design a visually rich interface to
browse the recommended web services, which enables a better understanding of
the service performance.
n Finally,
we conduct comprehensive experiments to evaluate our approach by employing real-world
web service QoS data set. More than 1.5 millions real world web service QoS
records from more than20 countries are used in our experiments.
ALGORITHMS USED:
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
ü Processor - Pentium –IV
ü Speed - 1.1
Ghz
ü RAM - 256
MB(min)
ü Hard Disk -
20 GB
ü Key Board -
Standard Windows Keyboard
ü Mouse - Two
or Three Button Mouse
ü Monitor - SVGA
SOFTWARE CONFIGURATION:-
ü Operating System : Windows XP
ü Programming Language :
JAVA
ü Java Version :
JDK 1.6 & above.
REFERENCE:
Xi Chen, Member, IEEE, Zibin Zheng,
Member, IEEE, Xudong Liu, Zicheng Huang, and Hailong Sun, Member, IEEE-“Personalized
QoS- Aware Web Service Recommendation and Visualization”-IEEE TRANSACTIONS ON
SERVICES COMPUTING, VOL. 6, NO. 1, JANUARY-MARCH 2013
