Personalized qos aware web service recommendation and visualization
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This document proposes a personalized QoS-aware web service recommendation system that uses a novel collaborative filtering algorithm and visualization technique. Existing recommendation systems do not account for variation in QoS based on user location and have poor time complexity. The proposed system combines model-based and memory-based collaborative filtering to improve accuracy and time complexity. It also provides a visualization of recommended services to improve user understanding of recommendations. The system was evaluated using a real-world dataset of over 1.5 million QoS records from more than 20 countries.
Personalized qos aware web service recommendation and visualization
- 1. 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.
- 2. 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. 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: First, we combine the model-based and memory based CF algorithms for web service recommendation, which significantly improves the
- 4. recommendation accuracy and time complexity compared with previous service recommendation algorithms. Second, we design a visually rich interface to browse the recommended web services, which enables a better understanding of the service performance. 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:
- 6. SYSTEM CONFIGURATION:- HARDWARE CONFIGURATION:- Processor - Pentium –IV Speed - 1.1 Ghz RAM - 256 MB(min)
- 7. Hard Disk - 20 GB Key Board - Standard Windows Keyboard Mouse - Two or Three Button Mouse Monitor - SVGA SOFTWARE CONFIGURATION:- Operating System : Windows XP Programming Language : JAVA 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