Information filtering via preferential diffusion
- Lü, Linyuan Web Sciences Center, University of Electronic Science and Technology of China, Chengdu, China - Department of Physics, University of Fribourg, Switzerland
- Liu, Weiping Department of Physics, University of Fribourg, Switzerland
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29.06.2011
Published in:
- Physical Review E - Statistical Nonlinear, and Soft Matter Physics. - 2011, vol. 83, no. 6, p. 066119
English
Recommender systems have shown great potential in addressing the information overload problem, namely helping users in finding interesting and relevant objects within a huge information space. Some physical dynamics, including the heat conduction process and mass or energy diffusion on networks, have recently found applications in personalized recommendation. Most of the previous studies focus overwhelmingly on recommendation accuracy as the only important factor, while overlooking the significance of diversity and novelty that indeed provide the vitality of the system. In this paper, we propose a recommendation algorithm based on the preferential diffusion process on a user-object bipartite network. Numerical analyses on two benchmark data sets, MovieLens and Netflix, indicate that our method outperforms the state-of-the-art methods. Specifically, it can not only provide more accurate recommendations, but also generate more diverse and novel recommendations by accurately recommending unpopular objects.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département de Physique
- Language
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- English
- Classification
- Physics
- License
- License undefined
- Identifiers
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- RERO DOC 27274
- DOI 10.1103/PhysRevE.83.066119
- Persistent URL
- https://folia.unifr.ch/unifr/documents/302090
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