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Effective mechanism for social recommendation of news 总被引:1,自引:0,他引:1
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 algorithms that are scalable with respect to the network’s size. Agent-based simulations reveal the features and the performance of these algorithms. To overcome the resultant drawbacks of each method we introduce two improved algorithms and show that they can optimize the network’s topology almost as fast and effectively as other not-scalable methods that make use of much more information. 相似文献
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Temporal effects in the growth of networks 总被引:1,自引:0,他引:1
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 resulting degree distribution shows an exponential, log-normal or power-law decay, which makes the model an apt candidate for modeling a wide range of real systems. 相似文献
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Duanbing?Chen An?ZengEmail author Giulio?Cimini Yi-Cheng?Zhang 《The European Physical Journal B - Condensed Matter and Complex Systems》2013,86(2):61
People in the Internet era have to cope with the information overload, striving to find what they are interested in, and usually face this situation by following a limited number of sources or friends that best match their interests. A recent line of research, namely adaptive social recommendation, has therefore emerged to optimize the information propagation in social networks and provide users with personalized recommendations. Validation of these methods by agent-based simulations often assumes that the tastes of users can be represented by binary vectors, with entries denoting users’ preferences. In this work we introduce a more realistic assumption that users’ tastes are modeled by multiple vectors. We show that within this framework the social recommendation process has a poor outcome. Accordingly, we design novel measures of users’ taste similarity that can substantially improve the precision of the recommender system. Finally, we discuss the issue of enhancing the recommendations’ diversity while preserving their accuracy. 相似文献
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Journal of Solution Chemistry - The effects induced by alkyl chain length on the thermal phase behavior for a series of imidazolium based ionic liquids (ILs) containing the anion... 相似文献
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Cimini A. Palumbo O. Simonetti E. De Francesco M. Appetecchi G. B. Fantini S. Lin R. Falgayrat A. Paolone A. 《Journal of Thermal Analysis and Calorimetry》2020,142(5):1791-1797
Journal of Thermal Analysis and Calorimetry - The thermal behaviour of selected ionic liquids, potentially useful as electrolyte components in electrochemical devices, formed by ammonium or... 相似文献
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G. Cimini M. Medo T. Zhou D. Wei Y.-C. Zhang 《The European Physical Journal B - Condensed Matter and Complex Systems》2011,80(2):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 necessary for a user to play a leading role
in the network. We further investigate the filtering efficiency of the model as well as its robustness against
malicious and spamming behaviour. We show that incorporating user reputation in the recommendation process can
substantially improve the outcome. 相似文献
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