Enhancing Book Recommendation with Side Information

Recommendation systems are being broadly adopted in various applications to suggest items of interest to users amidst the enormous volume of available information. And many academic libraries have implemented various recommendation technologies to attract more readers and evaluate the resource utilization. And collaborative filtering (CF) technologies are widely used. However, one key issue limiting the success of collaborative filtering in certain application domains is the cold-start problem. In this paper, we aim to solve this problem with side information including the profile of the readers and the information of the books. We propose three approaches: the first is a recommendation method based on readers' side information, the second one is based on the books' side information, the third one contains two methods to combine the side information and the rating information. And the experiments evaluated on the real dataset which is very sparse validate the efficiency of the methods.