Université de Fribourg

A generalized model via random walks for information filtering

Ren, Zhuo-Ming ; Kong, Yixiu ; Shang, Ming-Sheng ; Zhang, Yi-Cheng

In: Physics Letters A, 2016, vol. 380, no. 34, p. 2608–2614

There could exist a simple general mechanism lurking beneath collaborative filtering and interdisciplinary physics approaches which have been successfully applied to online E-commerce platforms. Motivated by this idea, we propose a generalized model employing the dynamics of the random walk in the bipartite networks. Taking into account the degree information, the proposed generalized model...

Université de Fribourg

Study of market model describing the contrary behaviors of informed and uninformed agents: Being minority and being majority

Zhang, Yu-Xia ; Liao, Hao ; Medo, Matúš ; Shang, Ming-Sheng ; Yeung, Chi Ho

In: Physica A: Statistical Mechanics and its Applications, 2016, vol. 450, p. 486–496

In this paper we analyze the contrary behaviors of the informed investors and uniformed investors, and then construct a competition model with two groups of agents, namely agents who intend to stay in minority and those who intend to stay in majority. We find two kinds of competitions, inter- and intra-groups. The model shows periodic fluctuation feature. The average distribution of...

Université de Fribourg

Similarity from multi-dimensional scaling: solving the accuracy and diversity dilemma in information filtering

Zeng, Wei ; Zeng, An ; Liu, Hao ; Shang, Ming-Sheng ; Zhang, Yi-Cheng

In: PLoS ONE, 2014, vol. 9, no. 10, p. e111005

Recommender systems are designed to assist individual users to navigate through the rapidly growing amount of information. One of the most successful recommendation techniques is the collaborative filtering, which has been extensively investigated and has already found wide applications in e-commerce. One of challenges in this algorithm is how to accurately quantify the similarities of user pairs...

Université de Fribourg

Information filtering in sparse online systems: recommendation via semi-local diffusion

Zeng, Wei ; Zeng, An ; Shang, Ming-Sheng ; Zhang, Yi-Cheng

In: PLoS ONE, 2013, vol. 8, no. 11, p. e79354

With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users’ decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot...

Université de Fribourg

Membership in social networks and the application in information filtering

Zeng, Wei ; Zeng, An ; Shang, Ming-Sheng ; Zhang, Yi-Cheng

In: The European Physical Journal B, 2013, vol. 86, no. 9, p. 1–7

During the past few years, users’ membership in the online system (i.e. the social groups that online users joined) were widely investigated. Most of these works focus on the detection, formulation and growth of online communities. In this paper, we study users’ membership in a coupled system which contains user-group and user-object bipartite networks. By linking users’ membership...

Université de Fribourg

Extracting the Information Backbone in Online System

Zhang, Qian-Ming ; Zeng, An ; Shang, Ming-Sheng

In: PLoS ONE, 2013, vol. 8, no. 5, p. e62624

Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite...

Université de Fribourg

Preference of online users and personalized recommendations

Guan, Yuan ; Zhao, Dandan ; Zeng, An ; Shang, Ming-Sheng

In: Physica A: Statistical Mechanics and its Applications, 2013, p. -

In a recent work [T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J.R. Wakeling, Y.-C. Zhang, Proc. Natl. Acad. Sci. 107 (2010) 4511], a personalized recommendation algorithm with high performance in both accuracy and diversity is proposed. This method is based on the hybridization of two single algorithms called probability spreading and heat conduction, which respectively are inclined to recommend...

Université de Fribourg

The reinforcing influence of recommendations on global diversification

Zeng, An ; Yeung, Chi Ho ; Shang, Ming-Sheng ; Zhang, Yi-Cheng

In: Europhysics Letters - EPL, 2012, vol. 97, no. 1, p. 18005

Recommender systems are promising ways to filter the abundant information in modern society. Their algorithms help individuals to explore decent items, but it is unclear how they distribute popularity among items. In this paper, we simulate successive recommendations and measure their influence on the dispersion of item popularity by Gini coefficient. Our result indicates that local diffusion and...

Université de Fribourg

Identifying influential nodes in complex networks

Lü, Linyuan ; Shang, Ming-Sheng ; Zhang, Yi-Cheng ; Zhou, Tao

In: Physica A: Statistical Mechanics and its Applications, 2011, vol. 391, no. 4, p. 1777–1787

Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational...