Differential Approaches to Improve Recommendation System: Issues and Challenges

Recommendation system which plays an important role in many applications as WWW, ecommerce etc. The main objective of this paper is to focus on various issues and challenges of recommendation system. Collaborative filtering is one of the techniques in recommender systems, providing personalized recommendations to users based on their previously expressed preferences in the form of ratings and those of other similar users. A recommender system uses Collaborative Filtering or Content-Based methods to predict new items of interest for a user. Although both methods have their own and distinct advantages but individually they fail to provide good recommendations in many situations. Incorporating components from collaborative and content based methods, can overcome these challenges like lack of data, data sparsity, stability, accuracy and correlation of traditional recommender systems. Inadequate ratings lot of time gives poor quality of recommendations in terms of accuracy. By giving the overview of these problems we can improve recommendations by approaching new methods and solutions, which can be used as a highway for research and practice in this area.