Issues in Personalized Recommendation for Web

Abstract

The development of personalized hybrid recommendation model is considered as challenging task due to many domain specific problems and many technical limits to work into integrated web environment. The novel idea of this research work is development of unique hybrid personalized recommendation model using graph data which could help to resolved cold start and sparsity issue. The presented model is combined with unique algorithm proposed here, which get succeed to generate results by integrating content-based, knowledge-based and preference based recommendation techniques. The proposed model is efficient, flexible and allows integration of multiple entities together. Along with that, this research work emphasis on study and analysis of various recommendation techniques, various issues related to recommendation system and various evaluation methodology related to recommendation system. The research work also describes various properties of graph data like flexibility, associativity operation, diversity of structure and its suitability to work with Big data. This investigation demonstrates use of graph structure for various recommendation algorithms are more appropriate. This research work also demonstrate implementation of various diverse graph models intended to design for various domains that helps in solving challenges related to real world problems. This research serve as guidelines to anyone who wants to implement recommendation system using graph data as integrated component of real time web environment for their computational challenges. newline