Evolution of recommender paradigm optimization over time

Abstract

In the past few decades recommender system has reshaped the way of information filtering between websites and the users. It helps in identifying user interest and generates product suggestions for the active users. This paper presents an enlightening analysis of various recommender system such as content-based, collaborative-based and hybrid recommendation techniques along with few optimization models that has been applied to improvise the parameters being considered by the aforementioned techniques. We explored 125 articles published from 1992 to 2019 in order to discuss the problems associated with the existing models. Various advantages and disadvantages of each recommendation model including the input methods has been elaborated. Critical review on research problems based on the explored techniques and future directions has also been covered.

Keywords

• Content filtering
• Collaborative filtering
• Hybrid filtering
• Optimization
• Similarity measures