Journal article

Adaptive social recommendation in a multiple category landscape

  • Chen, Duanbing Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
  • Zeng, An Physics Department, University of Fribourg, Switzerland
  • Cimini, Giulio Physics Department, University of Fribourg, Switzerland
  • Zhang, Yi-Cheng Physics Department, University of Fribourg, Switzerland
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    18.02.2013
Published in:
  • The European Physical Journal B. - 2013, vol. 86, no. 2, p. 1-8
English 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.
Faculty
Faculté des sciences et de médecine
Department
Département de Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/303088
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