Qos and Keyword-Aware Service Recommendation Method on Map Reduce for Big Data Applications

Similar to most big data applications, the big data tendency also poses heavy impacts on service recommender systems. With the growing number of alternative services, effectively recommending services that users preferred has became an important research issue. Service recommender systems have been exposed as valuable tools to help users deal with services overload and provide appropriate recommendations to them. In KASR, keywords are used to indicate users' preferences, and a user-based Collaborative filtering algorithm is adopted to generate appropriate recommendations. More specifically, a keyword-candidate list and domain thesaurus are provided to help obtain users' preferences. The active user gives his/her preferences by selecting the keywords from the keyword-candidate list, and the preferences of the previous users can be extracted from their reviews for services according to the keyword-candidate list and domain thesaurus. The proposed system proposes methods it aims at presenting a personalized service recommendation list and recommending the most appropriate service(s) to the users. To improve the scalability and efficiency of KASR in “Big Data” environment, the proposed system proposes techniques that have been implemented it on a Map Reduce framework in Hadoop platform. It improves the recommendation accuracy by considering the location of the user while recommend the service.