Evolution properties of online user preference diversity
-
Guo, Qiang
Research Center of Complex Systems Science, University of Shanghai for Science and Technology, China
-
Ji, Lei
Research Center of Complex Systems Science, University of Shanghai for Science and Technology, China
-
Liu, Jian-Guo
Research Center of Complex Systems Science, University of Shanghai for Science and Technology, China - Data Science and Cloud Service Centre, Shanghai University of Finance and Economics, China - Department of Physics, University of Fribourg, Switzerland
-
Han, Jingti
Data Science and Cloud Service Centre, Shanghai University of Finance and Economics, China
Show more…
Published in:
- Physica A: Statistical Mechanics and its Applications. - 2017, vol. 468, p. 698–713
English
Detecting the evolution properties of online user preference diversity is of significance for deeply understanding online collective behaviors. In this paper, we empirically explore the evolution patterns of online user rating preference, where the preference diversity is measured by the variation coefficient of the user rating sequence. The statistical results for four real systems show that, for movies and reviews, the user rating preference would become diverse and then get centralized finally. By introducing the empirical variation coefficient, we present a Markov model, which could regenerate the evolution properties of two online systems regarding to the stable variation coefficients. In addition, we investigate the evolution of the correlation between the user ratings and the object qualities, and find that the correlation would keep increasing as the user degree increases. This work could be helpful for understanding the anchoring bias and memory effects of the online user collective behaviors.
-
Faculty
- Faculté des sciences et de médecine
-
Department
- Département de Physique
-
Language
-
-
Classification
-
Physics
-
License
-
License undefined
-
Identifiers
-
-
Persistent URL
-
https://folia.unifr.ch/unifr/documents/305420
Statistics
Document views: 58
File downloads: