An approach to improving group recommendation systems based on latent factor matrices
Le Nguyen Hoai Nam, Ho Thi Hoang Vy, Le Hoang Thi My, Le Thi Tuyet Mai, Hong Tiet Gia, Ho Le Thi Kim Nhung · 2019
Group activities have been becoming more powerful in various fields today. This results in expanding single user recommendation systems to group recommendation systems. An effective approach to group recommendation systems is to represent a group as a virtual user. Then, the single user recommendations are performed for this virtual user. In this paper, we propose a novel virtual user computation named the observed-filled-rating-based method and its extended version. The aim of our research is to overcome the weaknesses of the previous methods in the virtual user computation to improve group recommendation systems.