In the era of internet, amount of data grown beyond the capacity of storing and processing, this is known as Big Data. When Users deals with Big Data it face varies difficulty at the time of needful data extraction. Therefore, the purpose of Smart Service Recommender system is providing appropriate recommendations to users as per their interest. In the past few years, the amount of online web data has increases explosively, yielding the big data processing and analysis problem for recommender systems. Consequently, most of the traditional service recommender systems frequently suffer from scalability and inefficiency problems when processing or analyzing such large volume data. Moreover, an existing service recommender system present the same ratings and rankings of items to different users without considering varied users¿ preferences, and therefore fails to meet users personalized requirements