Survey on Collaborative and Content-based Recommendation Systems

Abstract

In recent times information available on Internet is growing at the exponential rate. This makes searching correct information very difficult and it is challenging to perform the same within shortest amount of time. In such context there is need of recommender systems which can help in information filtering and promoting the information which is likely as per corresponding user’s interest. Recommender systems have become popular in widespread applications and many researchers are exploring it to make them more efficient and effective. Though recommendations systems are being used for a quite long time, many research challenges and issues in the design of effective recommendation systems are yet to be addressed in an effective manner. This paper discusses research challenges, opportunities and possible applications of recommender systems. Recommendation systems are categorized into collaborative and content-based filtering. This paper briefs working of both types of recommender systems and also discusses research challenges within them. The collaborative filtering approach suffers from many drawbacks, including data sparsity, gray sheep, cold start problem, and scalability. The content-based filtering approach suffers from reciprocity, sparsity and limited content analysis issues. Also, future research directions in collaborative filtering and content-based recommendation systems discussed. Various application domains have listed out where recommendation systems can be improved, such as healthcare, agriculture, etc. The paper describes possible future extensions in all these applications. Overall this paper will act as a guide for those who are interested in doing research in the recommendation system.