BibTeX:

@article{IJIRSTV2I2047,
     title={Personalized Movie Recommender System using Rank Boosting Approach on Hadoop},
     author={Annies V Jose and Jini K M},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={2},
     number={2},
     pages={126--130},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV2I2047.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

Today we are living in an era of Big Data. Large numbers of services are available to customers; from these services it is difficult for them to choose those that are most appropriate for them. In this scenario a wide variety of service recommender systems will guide the user in selecting the most appropriate one. But these traditional service recommender systems will not work well with Big Data environment; they will experience scalability and efficiency problems as it has to work on huge amount of data. Most of the existing recommender system will provide same rating and ranking of services to different customers. As a solution to this we propose service recommender systems that operate on MapReduce Framework on Hadoop platform. Our service recommender system uses keywords to indicate user preferences and a variation of collaborative filtering algorithm called user based collaborative filtering is used to provide recommendation to customers. It also uses rank boosting approach for combined preferences, which will help to rank the results according to the preference of the current user and produce the recommendation list accordingly.

Efficiency, MapReduce, RankBoosting, Scalability, User-based Collaborative Filtering