Examining the Collaborative Filtering-Based Recommender System: Approaches and Improvements

On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant in-formation in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores collaborative filtering recommender system along with its approaches. Further the paper is intended to analyze various extending capabilities which can be incorporated into collaborative based recommender approach to improve the accuracy as well as to overcome many of the challenges faced by traditional collaborative based recommender system. Comparative analysis of extending capabilities along with its merits, demerits and applications are highlighted in this paper.