987 citations · 2.3k across the 33 of their papers we have counts for
9 papers · 1 filter
Solving the accuracy-diversity dilemma via directed random walks
Jian-Guo Liu, Kerui Shi, Qiang Guo
Random walks have been successfully used to measure user or object similarities in collaborative filtering (CF) recommender systems, which is of high accuracy but low diversity. A…
Information filtering via biased heat conduction
Jian-Guo Liu, Tao Zhou, Qiang Guo
Heat conduction process has recently found its application in personalized recommendation [T. Zhou \emph{et al.}, PNAS 107, 4511 (2010)], which is of high diversity but low accurac…
Information Filtering via Implicit Trust-based Network
Zhao-Guo Xuan, Zhan Li, Jian-Guo Liu
Based on the user-item bipartite network, collaborative filtering (CF) recommender systems predict users' interests according to their history collections, which is a promising way…
Degree correlation effect of bipartite network on personalized recommendation
Jian-Guo Liu, Tao Zhou, Zhao-Guo Xuan +3
In this paper, by introducing a new user similarity index base on the diffusion process, we propose a modified collaborative filtering (MCF) algorithm, which has remarkably higher…
Effect of user tastes on personalized recommendation
Jian-Guo Liu, Tao Zhou, Qiang Guo +2
In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algo…
Highly accurate recommendation algorithm based on high-order similarities
Jian-Guo Liu, Tao Zhou, Bing-Hong Wang +1
In this Letter, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead…