Personalized Recommendation Algorithm Using User Demography Information
Yae Dai, HongWu Ye, Songjie Gong · 2009
Personalized recommendation systems are web-based systems that aim at predicting a user’s interest on available products and services by relying on previously rated items and dealing with the problem of information and product overload. User demography information associated with a user’s personality is rarely considered in the personalization process, especially in the collaborative filtering (CF) which is the very important technology in the recommendation systems. In this paper, a new collaborative filtering personalized recommendation algorithm is proposed which applies the user demography information. This method combines the rating similarity and the user demography similarity in the recommendation process to improve the prediction accuracy by efficiently managing the problem of data sparsity. The experiments suggest that collaborative filtering based on combining similarity provide better recommendation quality than collaborative filtering based on only rating similarity dramatically.