Information filtering by similarity-preferential diffusion processes
- Zeng, An Department of Physics, University of Fribourg, Switzerland
- Vidmer, Alexandre Department of Physics, University of Fribourg, Switzerland
- Medo, Matúš Department of Physics, University of Fribourg, Switzerland
- Zhang, Yi-Cheng Department of Physics, University of Fribourg, Switzerland
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01.03.2014
Published in:
- EPL (Europhysics Letters). - 2014, vol. 105, no. 5, p. 58002
English
Recommender systems provide a promising way to address the information overload problem which is common in online systems. Based on past user preferences, a recommender system can find items that are likely to be relevant to a given user. Two classical physical processes, mass diffusion and heat conduction, have been used to design recommendation algorithms and a hybrid process based on them has been shown to provide accurate and diverse recommendation results. We modify both processes as well as their hybrid by introducing a parameter which can be used to enhance or suppress the weight of users who are most similar to the target user for whom the recommendation is done. Evaluation on two benchmark data sets demonstrates that both recommendation accuracy and diversity are improved for a wide range of parameter values. Threefold validation indicates that the achieved results are robust and the new recommendation methods are thus applicable in practice.
- 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 210196
- DOI 10.1209/0295-5075/105/58002
- Persistent URL
- https://folia.unifr.ch/unifr/documents/303793
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