Service recommender systems are valuable tools for providing appropriate recommendations to users. In the last decade the rapid growth of the number of customers, services and other online information yields service recommender systems in Big Data environment, some critical challenges .Traditional service recommender systems often suffer from scalability and inefficiency problems when processing or analyzing such large scale data. Moreover, most of the existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements . KASR(Keyword Aware Service Recommendation System) aims at calculating a personalized rating of each candidate service for a user by extracting keywords from user reviews, and then presenting a personalized service recommendation list and recommending the most appropriate services to users. Various limitations of the current recommendation methods can be reduced by possible extensions that can provide better recommendation capabilities. These extensions include incorporation of the contextual information into the recommendation process. Designing and implementing scalable recommender systems in Big Data environment solve the scalability problem.

Keyword Aware Service Recommendation System , Collaborative Filtering, BigData

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