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Journal article

Preference of online users and personalized recommendations

  • Guan, Yuan Web Science Center, University of Electronic Science and Technology of China, Chengdu, China
  • Zhao, Dandan Web Science Center, University of Electronic Science and Technology of China, Chengdu, China
  • Zeng, An Department of Physics, University of Fribourg, Switzerland
  • Shang, Ming-Sheng Web Science Center, University of Electronic Science and Technology of China, Chengdu, China - Institute of Information Economy, Alibaba Business School, Hangzhou Normal University, China
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    2013
Published in:
  • Physica A: Statistical Mechanics and its Applications. - 2013, vol. 392, no. 16, p. 3417-3423
Hybrid algorithm
Recommender system
Personalized parameter
User heterogeneity
English In a recent work [T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J.R. Wakeling, Y.-C. Zhang, Proc. Natl. Acad. Sci. 107 (2010) 4511], a personalized recommendation algorithm with high performance in both accuracy and diversity is proposed. This method is based on the hybridization of two single algorithms called probability spreading and heat conduction, which respectively are inclined to recommend popular and unpopular products. With a tunable parameter, an optimal balance between these two algorithms in system level is obtained. In this paper, we apply this hybrid method in individual level, namely each user has his/her own personalized hybrid parameter to adjust. Interestingly, we find that users are quite different in personalized hybrid parameters and the recommendation performance can be significantly improved if each user is assigned with his/her optimal personalized hybrid parameter. Furthermore, we find that users’ personalized parameters are negatively correlated with users’ degree but positively correlated with the average degree of the items collected by each user. With these understandings, we propose a strategy to assign users with suitable personalized parameters, which leads to a further improvement of the original hybrid method. Finally, our work highlights the importance of considering the heterogeneity of users in recommendation.
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/303076
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