CSRS: Customized service recommendation system for big data analysis using map reduce

Recommender systems have been important helpful software system techniques and tools giving desirable recommendation to items that are valuable to end-users. In past era, the measure of services, users, and online data have expanded very quickly, so it recognizes the Big data that is the reason there is examination issue for recommender systems. As a result, efficiency and scalability are issues in existing service recommender systems when the analyzing or processing Big Data. Also, the same rankings and ratings are provided by existing service recommender systems to various users without counting his/her distinctive preferences and in this manner, doesn't meet user's customized essentials. In this paper, it's proposed to utilize service recommendation techniques, which goes for showing a customized service recommendation set and recommender systems are recommending the foremost valuable services to end-users. To improve efficiency and scalability issue in massive data surroundings, it is implemented in hadoop framework which uses map reduce parallel processing model.