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...
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In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 2012, vol. 85, no. 4, p. 046108
The advent of the Internet and World Wide Web has led to unprecedent growth of the information available. People usually face the information overload by following a limited number of sources which best fit their interests. It has thus become important to address issues like who gets followed and how to allow people to discover new and better information sources. In this paper we conduct an...
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In: Physical Review Letters, 2011, vol. 107, no. 23, p. 238701
We show that to explain the growth of the citation network by preferential attachment (PA), one has to accept that individual nodes exhibit heterogeneous fitness values that decay with time. While previous PA-based models assumed either heterogeneity or decay in isolation, we propose a simple analytically treatable model that combines these two factors. Depending on the input assumptions, the...
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In: Europhysics Letters, 2011, vol. 96, no. 1, p. 18004
We introduce a framework for network analysis based on random walks on directed acyclic graphs where the probability of passing through a given node is the key ingredient. We illustrate its use in evaluating the mutual influence of nodes and discovering seminal papers in a citation network. We further introduce a new similarity metric and test it in a simple personalized recommendation process....
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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: PLoS ONE, 2011, vol. 6, no. 7, p. e20648
The study of the organization of social networks is important for the understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this...
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In: Physica A: Statistical Mechanics and its Applications, 2011, vol. 390, no. 9, p. 1635-1645
The growth-optimal portfolio optimization strategy pioneered by Kelly is based on constant portfolio rebalancing which makes it sensitive to transaction fees. We examine the effect of fees on an example of a risky asset with a binary return distribution and show that the fees may give rise to an optimal period of portfolio rebalancing. The optimal period is found analytically in the case of...
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In: Quantitative Finance, 2010, vol. 10, no. 7, p. 689-697
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Thèse de doctorat : Université de Fribourg, 2008 ; no 1614.
Knowledge and information make our lives easier and more enjoyable. In this thesis we use techniques and concepts from statistical physics and probability theory to explore several models of complex systems where information plays a prominent role. When building these models we tried to capture main aspects of real systems but also to keep the models simple and analytically solvable. We begin the...
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In: Proceedings of the National Academy of Sciences of the USA - PNAS, 2010, vol. 107, no. 10, p. 4511-4515
Recommender systems use data on past user preferences to predict possible future likes and interests. A key challenge is that while the most useful individual recommendations are to be found among diverse niche objects, the most reliably accurate results are obtained by methods that recommend objects based on user or object similarity. In this paper we introduce a new algorithm specifically to...
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