Hybrid Recommendation System

Recommendation systems are being used in more and more fields to filter and suggest to users with items they may be interested in clicking or purchasing. There are many recommending algorithms and, the two most widely used recommending algorithms are content-based and collaborative filtering. However, both two algorithms have problems and drawbacks. In recent years, many research papers have focused on using a hybrid recommendation system to solve the problems of solely using content-based or collaborative filtering and improve recommendation accuracy. Thus, it is necessary to do a summary of these recent research papers. In this review paper, we are going to discuss the recent progress and current status of the research area of hybrid recommendation systems including what hybrid recommendation systems are being used, how different algorithms are combined, what new combinations of recamiers are being researched, and how hybrid recommendation systems’ performance can be improved. Further, we will discuss current existing challenges, problems, and possible and important future research directions.