Volume 116, Number 3, November 2016
|Number of page(s)||7|
|Published online||21 December 2016|
The essential role of time in network-based recommendation
Department of Physics, University of Fribourg - Chemin du Muse 3, CH-1700 Fribourg, Switzerland
Received: 10 June 2016
Accepted: 5 December 2016
Random walks on bipartite networks have been used extensively to design personalized recommendation methods. While aging has been identified as a key component in the growth of information networks, most research has focused on the networks' structural properties and neglected the often available time information. Time has been largely ignored both by the investigated recommendation methods as well as by the methodology used to evaluate them. We show that this time-unaware approach overestimates the methods' recommendation performance. Motivated by microscopic rules of network growth, we propose a time-aware modification of an existing recommendation method and show that by combining the temporal and structural aspects, it outperforms the existing methods. The performance improvements are particularly striking in systems with fast aging.
PACS: 07.05.Kf – Data analysis: algorithms and implementation; data management / 89.20.-a – Interdisciplinary applications of physics / 89.20.Ff – Computer science and technology
© EPLA, 2016
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