Université de Fribourg

Recommender systems

Lü, Linyuan ; Medo, Matúš ; Yeung, Chi Ho ; Zhang, Yi-Cheng ; Zhang, Zi-Ke ; Zhou, Tao

In: Physics Reports, 2012, vol. 519, no. 1, p. 1–49

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification...

Université de Fribourg

Effective mechanism for social recommendation of news

Wei, Dong ; Zhou, Tao ; Cimini, Giulio ; Wu, Pei ; Liu, Weiping ; Zhang, Yi-Cheng

In: Physica A: Statistical Mechanics and its Applications, 2011, vol. 390, no. 11, p. 2117-2126

Recommender systems represent an important tool for news distribution on the Internet. In this work we modify a recently proposed social recommendation model in order to deal with no explicit ratings of users on news. The model consists of a network of users which continually adapts in order to achieve an efficient news traffic. To optimize the network’s topology we propose different stochastic...

Université de Fribourg

Effects of high-order correlations on personalized recommendations for bipartite networks

Liu, Jian-Guo ; Zhou, Tao ; Che, Hong-An ; Wang, Bing-Hong ; Zhang, Yi-Cheng

In: Physica A, 2010, vol. 389, no. 4, p. 881-886

In this paper, we introduce a modified collaborative filtering (MCF) algorithm, which has remarkably higher accuracy than the standard collaborative filtering. In the MCF, instead of the cosine similarity index, the user–user correlations are obtained by a diffusion process. Furthermore, by considering the second-order correlations, we design an effective algorithm that depresses the influence...

Université de Fribourg

Collaborative filtering based on multi-channel diffusion

Shang, Ming-Sheng ; Jin, Ci-Hang ; Zhou, Tao ; Zhang, Yi-Cheng

In: Physica A: Statistical Mechanics and its Applications, 2009, vol. 388, no. 23, p. 4867-4871

In this paper, by applying a diffusion process, we propose a new index to quantify the similarity between two users in a user–object bipartite graph. To deal with the discrete ratings on objects, we use a multi-channel representation where each object is mapped to several channels with the number of channels being equal to the number of different ratings. Each channel represents a certain...