Abinaya R1, Selva Lakshmi C B
2
1PG student- Department of CSE - Vellammal college of
Engineering and technology, Madurai, Tamilnadu, India.
2Assistant Professor - Department of CSE- Vellammal
college of Engineering and technology, Madurai, Tamilnadu,
India.
A recommendation system helps
us to generate the appropriate service to users. In recent years, the amount of
data in our world has been raising quickly, to yield the big data analysis
problem. As a result, service recommender system rapidly suffers from
inefficiency and scalability problems when processing large scale data. In
addition, most of the existing approach provides service recommendations based
on the user preference without considering users combined preference. For this
purpose, rank boosting algorithm is proposed for combined preferences to
generate appropriate recommendations. It aims at getting the input as combined
preferences, based on the preferences it process the similarities with the
reviews of the existing users then it provides the ranking to the services.
Based on the ranking it generates the recommendation list as result to the end
users for their combined preference. To improve its scalability and efficiency
in big data environment, user preferences based Service recommendation
system is implemented on a distributed computing platform, hadoop which uses
Map Reduce as computing framework.
