Hybrid recommendation model based on incremental collaborative filtering and content-based algorithms

Haiming Wang, Peng Zhang, Tun Lu, Hansu Gu, Ning Gu

Abstract: In the past decade, online news consumption has been steadily growing. New articles are published every few minutes, and user preferences are also constantly changing. Traditional recommender systems update model at regular intervals, which cannot adjust recommendation list dynamically according to the changes of user preferences. In this paper, we propose a hybrid recommendation model which contains two key components: incremental update item-based collaborative filtering (CF) and latent semantic analysis based relative term frequency algorithms. The hybrid recommendation model adjusts recommendation list dynamically by updating similarity table of items incrementally in incremental update item-based CF module, moreover, combining collaborative filtering and content-based algorithm ensures the relevance of recommendation articles. Results show that our proposed hybrid recommendation model outperforms traditional recommender approaches.