A Survey of Recent Articles in the Field of Recommendation Diversification

Today, with the explosive growth of the internet applications and in the current age of information overload, recommender systems are steadily becoming more important in filtering relevant information and items for users and also in keeping customers and gaining more benefits. Based on the assumption that users with similar preferences in history would also have similar interests in the future, collaborative filtering algorithms have shown significant successes and become one of the most pervasive branches in the study of personalized recommendation. However, while most research focused on improving the accuracy of recommender systems, other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. The ability of recommending a diverse set of items is very important for user satisfaction, because it gives users a richer set of items to choose from and increases the chance of discovering new items. With the development of electronic markets and arrival of diverse and new goods, addressing the diversity factor has become very important and received a lot of attentions in recommender systems literature. But there is a big vacancy in the classification of the works done in this area which is necessary in order to clarify and streamline the directions for future research. Therefore, in this paper, the existing approaches and some of the authentic papers published in recent two or three years are introduced.