Performance Evaluation of Knowledge Based Collaborative Filtering Model

Collaborative filtering Recommender systems apply data mining techniques to produce personalized recommender system during the online interaction of active users. These systems use variety of techniques for achieving high success on business, banking, finance and other domains. The fast increase in users and products in recent years produces some of the key issues and challenges for recommender systems. These include scalability, sparsity and producing high quality of recommendations. Hence there is a necessity to produce new recommender systems to provide high accurate and fast recommenders systems to handle large mass of data. The issues and challenges are improved by introducing knowledge based proximity model using clustering technique. The performance of the model is experimentally evaluated with different statistical and decision making metrics using real-world datasets.