Recommender Systems: Collaborative Filtering and Content-based Recommender System
The principal goal of this paper is to try to ascertain which algorithm has the highest precision, after training based on the same dataset, and observe itemCF contains the most accurate rate.
Abstract
There are three algorithms of recommender systems proposed by this paper, which are item collaborative filtering(itemCF), user collaborative filtering(useCF) and content-based recommender system(CBRS). The principal goal of this paper is to try to ascertain which algorithm has the highest precision, after training based on the same dataset. In accordance with the data we chose and ceaseless testing, we observe itemCF contains the most accurate rate. However, we theoretically and empirically conceive each algorithm owns different advantages and drawbacks, should be used in the specific circumstance.