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1. Location-Aware and Personalized Collaborative Filtering
for Web Service Recommendation
ABSTRACT:
Collaborative Filtering (CF) is widely employed for making Web service
recommendation. CF-based Web service recommendation aims to predict missing
QoS (Quality-of-Service) values of Web services. Although several CF-based Web
service QoS prediction methods have been proposed in recent years, the
performance still needs significant improvement. Firstly, existing QoS prediction
methods seldom consider personalized influence of users and services when
measuring the similarity between users and between services. Secondly, Web
service QoS factors, such as response time and throughput, usually depends on the
locations of Web services and users. However, existing Web service QoS
prediction methods seldom took this observation into consideration. In this paper,
we propose a location-aware personalized CF method for Web service
recommendation. The proposed method leverages both locations of users and Web
services when selecting similar neighbors for the target user or service. The
method also includes an enhanced similarity measurement for users and Web
services, by taking into account the personalized influence of them. To evaluate the
performance of our proposed method, we conduct a set of comprehensive
experiments using a real-world Web service dataset. The experimental results

2. indicate that our approach improves the QoS prediction accuracy and
computational efficiency significantly, compared to previous CF-based methods.
EXISTING SYSTEM:
 QoS is usually defined as a set of non-functional properties, such as response
time, throughput, reliability, and so on. Due to the paramount importance of
QoS in building successful service-oriented applications, QoS-based Web
service discovery and selection has garnered much attention from both
academia and industry.
 Typically, a user prefers to select a Web service with the best QoS
performance, provided that a set of Web service candidates satisfying his/her
functional requirements are discovered. In reality, however, it is neither easy
nor practical for a user to acquire the QoS for all Web service candidates,
due to the following reasons: Web service QoS is highly depend on both
users’ and Web services’ circumstances.
 Therefore, the observed QoS of the same Web service may be different from
user to user. Con-ducting real-world Web service evaluation for obtaining
QoS of Web service candidates is both time-consuming and resource-
consuming.

3. DISADVANTAGES OF EXISTING SYSTEM:
 It is impractical for a user to acquire QoS information by invoking all of the
service candidates. And some QoS properties (e.g., reputation and
reliability) are difficult to be evaluated, since they require both long
observation duration and a large number of invocations. These challenges
call for more effective approaches to acquire service QoS information.
 Previous CF-based Web service recommendation methods have rarely taken
into account the peculiar characteristics of Web service QoS when making
QoS predictions.
 QoS attributes of Web services such as response time and throughput highly
depend on the underlying network conditions, which, however, are usually
ignored by the previous work.
PROPOSED SYSTEM:
 We proposed an enhanced measurement for compu-ting QoS similarity
between different users and between different services. The measurement
takes into account the personalized deviation of Web services’ QoS and
users’ QoS experiences, in order to improve the accuracy of similarity
computation. Based on the above enhanced similarity measurement, we

4. proposed a location-aware CF-based Web service QoS prediction method for
service recommendation.
 We conducted a set of comprehensive experiments employing a real-world
Web service dataset, which demonstrated that the proposed Web service
QoS prediction method significantly outperforms previous well-known
methods. We also incorporate the locations of both Web services and users
into similar neighbor selection, for both Web services and users.
Comprehensive experiments conducted on a real Web service dataset
indicate that our method significantly outperforms previous CF-based Web
service recommendation methods.
ADVANTAGES OF PROPOSED SYSTEM:
 Our location-aware QoS prediction method has a solid basis, because of the
strong relation between the locations of users (or Web services) and the Web
services’ QoS perceived by the users.
 We conducted an experiment to evaluate the impact of data sparseness on
the prediction coverage, in which, our proposed methods (including
ULACF, ILACF and HLACF) were compared with the traditional CF
methods such as UPCC and IPCC. We find that, our methods can always
achieve nearly 100% prediction coverage, when the matrix density varies

5. from 5% to 30%. By contrast, the traditional CF methods have significantly
lower prediction coverage, especially when K is small.
 Achieves aiming at improving the QoS prediction performance, we take into
account the personal QoS characteristics of both Web services and users to
compute similarity between them.
SYSTEM ARCHITECTURE:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
 System : Pentium IV 2.4 GHz.

6.  Hard Disk : 40 GB.
 Floppy Drive : 1.44 Mb.
 Monitor : 15 VGA Colour.
 Mouse : Logitech.
 Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
 Operating system : Windows XP/7.
 Coding Language : ASP.net, C#.net
 Tool : Visual Studio 2010
 Database : SQL SERVER 2008
REFERENCE:
Jianxun Liu, Mingdong Tang, Member, IEEE, Zibin Zheng, Member, IEEE,
Xiaoqing (Frank) Liu, Member, IEEE, Saixia Lyu, “Location-Aware and
Personalized Collaborative Filtering for Web Service Recommendation”, IEEE
TRANSACTIONSON SERVICES COMPUTING2015.