User Based Personalized Search with Big Data

To develop a -Aware Service Recommendation method, named KASR, to address scalability and inefficiency problem in Big Data with traditional service recommender systems, which fails to meet users' personalized requirements and diverse Preferences. Moreover, most of 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. Current recommendation methods usually can be classified into three main categories content-based, collaborative, and hybrid recommendation approaches. Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficiency problems when processing or analysing such large-scale data.