Location Based Service Recommendation System Using Hierarchy Clustering Techniques

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2016 by IJCTT Journal
Volume-36 Number-2
Year of Publication : 2016
Authors : Ravikumar, Kaliraj, Boomathi
  10.14445/22312803/IJCTT-V36P114

MLA

Ravikumar, Kaliraj, Boomathi "Location Based Service Recommendation System Using Hierarchy Clustering Techniques". International Journal of Computer Trends and Technology (IJCTT) V36(2):81-86, June 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Recommendation techniques aim to support the users in their decision-making while the users interact with large information spaces. Recommendation has been a hot research topic with the rapid growth of information. In the field of services computing and cloud computing, efficient and effective recommendation techniques are critical in helping designers and developers to analyse the available information intelligently for better application design and development. To recommend Web services that best fit a user’s need, QoS values which characterize the non-functional properties of those candidate services are in demand. But in reality, the QoS information of Web service is not easy to obtain, because only limited historical invocation records exist. So in this project present a model named CLUS for reliability prediction of atomic Web services, which estimates the reliability for an on going service invocation based on the data merged from previous invocations. Then aggregates the past invocation data using hierarchy clustering algorithm to achieve better scalability comparing with other current approaches. In addition, the paper proposes a model-based collaborative filtering and location based recommendation approach based on supervised learning technique and linear regression to estimate the missing reliability values.

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Keywords
hierarchy clustering, Prediction, Qos, Recommendation, Model-based collaborative filtering, Web services