Hybrid Recommender System Using Semi-supervised Clustering Based on Gaussian Mixture Model - Yihao Zhang; Xiaoyang Liu; Wanping Liu; Changpeng Zhu (2016 International Conference on Cyberworlds (CW), 2016)
Recommender systems are used to make recommendations about products, information, or services for users. Most existing recommender systems implicitly assume one particular type of user behavior. However, other recommender system utilizes different particular type information by combining different techniques to improve the quality of the recommendation. This paper proposed a novel personalized recommendation method that utilizes semi-supervised clustering based Gaussian mixture model, which provides a hybrid recommender method by combining demographic method and user-based collaborative filtering method. The result from various simulations using MovieLens data set shows that the proposed recommender method performs better and helps to improve the quality of recommendation rating.