In: Physica A: Statistical Mechanics and its Applications, 2018, vol. 499, p. 490–497
Understanding the patterns of collective behavior in online social network (OSNs) is critical to expanding the knowledge of human behavior and tie relationship. In this paper, we investigate a specific pattern called social signature in Facebook and Wiki users’ online communication behaviors, capturing the distribution of frequency of interactions between different alters over time in the...
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In: PLoS ONE, 2014, vol. 9, no. 5, p. e97146
How to design an accurate and robust ranking algorithm is a fundamental problem with wide applications in many real systems. It is especially significant in online rating systems due to the existence of some spammers. In the literature, many well-performed iterative ranking methods have been proposed. These methods can effectively recognize the unreliable users and reduce their weight in judging...
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In: EPL (Europhysics Letters), 2014, vol. 105, no. 5, p. 58002
Recommender systems provide a promising way to address the information overload problem which is common in online systems. Based on past user preferences, a recommender system can find items that are likely to be relevant to a given user. Two classical physical processes, mass diffusion and heat conduction, have been used to design recommendation algorithms and a hybrid process based on them has...
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In: The European Physical Journal B - Condensed Matter and Complex Systems, 2011, vol. 80, no. 2, p. 201-208
Recommender systems help people cope with the problem of information overload. A recently proposed adaptive news recommender model [M. Medo, Y.-C. Zhang, T. Zhou, Europhys. Lett. 88, 38005 (2009)] is based on epidemic-like spreading of news in a social network. By means of agent-based simulations we study a “good get richer” feature of the model and determine which attributes are...
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In: Quantitative Finance, 2010, vol. 10, no. 7, p. 689-697
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In: Europhysics Letters, 2009, vol. 88, no. 6, p. 68008
In some recommender systems where users can vote objects by ratings, the similarity between users can be quantified by a benchmark index, namely the Pearson correlation coefficient, which reflects the rating correlations. Another alternative way is to calculate the similarity based solely on the relevance information, namely whether a user has voted an object. The former one uses more information...
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In: Europhysics Letters, 2008, vol. 82, no. 5, p. 58007
Recommender systems are significant to help people deal with the world of information explosion and overload. In this letter, we develop a general framework named self-consistent refinement and implement it by embedding two representative recommendation algorithms: similarity-based and spectrum-based methods. Numerical simulations on a benchmark data set demonstrate that the present method...
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In: Physica A: Statistical Mechanics and its Applications, 2006, vol. 365, no. 2, p. 529-542
Minority games where groups of agents remember, react or incorporate information with different timescales are investigated. We support our findings by analytical arguments whenever possible.
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In: Physica A: Statistical and Theoretical Physics, 2006, vol. 371, no. 2, p. 732-744
Advances in information technology reduce barriers to information propagation, but at the same time they also induce the information overload problem. For the making of various decisions, mere digestion of the relevant information has become a daunting task due to the massive amount of information available. This information, such as that generated by evaluation systems developed by various web...
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In: Europhysics Letters, 2006, vol. 75, no. 6, p. 1006-1012
With the explosive growth of accessible information, expecially on the Internet, evaluation-based filtering has become a crucial task. Various systems have been devised aiming to sort through large volumes of information and select what is likely to be more relevant. In this letter we analyse a new ranking method, where the reputation of information providers is determined self-consistently.
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