Efficient Query Analyzer with Personalized Recommendations

This paper deals with the development of flexible recommender systems, to make real time computations on vast amount of data. Even if most of the research has been dedicated to improve accuracy of recommendations, scalability is a serious issue. To address these issues, a QueRIE framework is developed to provide suggestions that are likely to interest users. QueRIE blends user characteristics into queries to generate personalized recommendations to user by prioritizing and filtering queries that are available. For this purpose this paper proposes a new approach called fragment based system in which tokenization of string is focused. In this paper, data management systems must provide powerful query management capabilities, from query browsing to automatic query recommendations. Here the session of active user is represented as strings. These recorded strings are used to identify similar query strings by means of string mapping in the previously recorded sessions, which are assembled together to form a complete query for the active user. So the system uses previous information of current user and previous users. Thus this system automates the process of suggesting recommendations. Finally a comparison of fragment based approach to the previously proposed method called tuple based approach is discussed.