Journal article

Avoiding congestion in recommender systems

  • Ren, Xiaolong Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, China - Department of Physics, University of Fribourg, Switzerland
  • Lü, Linyuan Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, China
  • Liu, Runran Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, China
  • Zhang, Jianlin Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, China - Department of Physics, University of Fribourg, Switzerland
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    01.06.2014
Published in:
  • New Journal of Physics. - 2014, vol. 16, no. 6, p. 063057
English Recommender systems use the historical activities and personal profiles of users to uncover their preferences and recommend objects. Most of the previous methods are based on objects' (and/or users') similarity rather than on their difference. Such approaches are subject to a high risk of increasingly exposing users to a narrowing band of popular objects. As a result, a few objects may be recommended to an enormous number of users, resulting in the problem of recommendation congestion, which is to be avoided, especially when the recommended objects are limited resources. In order to quantitatively measure a recommendation algorithmʼs ability to avoid congestion, we proposed a new metric inspired by the Gini index, which is used to measure the inequality of the individual wealth distribution in an economy. Besides this, a new recommendation method called directed weighted conduction (DWC) was developed by considering the heat conduction process on a user–object bipartite network with different thermal conductivities. Experimental results obtained for three benchmark data sets showed that the DWC algorithm can effectively avoid system congestion, and greatly improve the novelty and diversity, while retaining relatively high accuracy, in comparison with the state-of-the-art methods.
Faculty
Faculté des sciences
Department
Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/303797
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