Service recommender system is most common in recent year and useful in variety of applications. It is provide appropriate recommendation to the user. In the last year the amount of user, services and online data has grown rapidly , the big data analysis the problem for service recommender system. Traditional recommender system was suffer from scalability and inefficiency problem when analysing large amount of data. Existing recommender system provide same rating and ranking of the services for different user but they does not consider users preferences and therefore they are fail to meet users specific requirement. In this paper, we propose the process of developing travel recommender system for address the above challenges. The aim of Keyword Aware Service Recommender System (KASR) is provide service recommendation list and give appropriate services recommendation to the user. It gives appropriate recommendation by collaborative filtering algorithm and keyword are used for indicating users preferences. To increase scalability and inefficiency , KASR is implemented on Hadoop by using Big Data and for parallel processing Map-Reduce paradigm is use. Finally, large experiment is conducted by using Real-World dataset , that produce result for KASR significantly improve accuracy and scalability of service recommender system over existing recommender system.
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