An item-oriented recommendation algorithm on cold-start problem
- Qiu, Tian School of Information Engineering, Nanchang Hangkong University - Nanchang, China
- Chen, Guang School of Information Engineering, Nanchang Hangkong University - Nanchang, China
- Zhang, Zi-Ke Web Sciences Center, University of Electronic Science and Technology of China - Chengdu, China - Institute of Information Economy, Hangzhou Normal University - Hangzhou, China - Department of Physics, University of Fribourg, Switzerland
- Zhou, Tao Web Sciences Center, University of Electronic Science and Technology of China - Chengdu, China - Department of Modern Physics, University of Science and Technology of China - Hefei, China
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18.08.2011
Published in:
- EPL - Europhysics Letters. - 2011, vol. 95, no. 5, p. 58003
English
Based on a hybrid algorithm incorporating the heat conduction and probability spreading processes (Proc. Natl. Acad. Sci. U.S.A., 107 (2010) 4511), in this letter, we propose an improved method by introducing an item-oriented function, focusing on solving the dilemma of the recommendation accuracy between the cold and popular items. Differently from previous works, the present algorithm does not require any additional information (e.g., tags). Further experimental results obtained in three real datasets, RYM, Netflix and MovieLens, show that, compared with the original hybrid method, the proposed algorithm significantly enhances the recommendation accuracy of the cold items, while it keeps the recommendation accuracy of the overall and the popular items. This work might shed some light on both understanding and designing effective methods for long-tailed online applications of recommender systems.
- 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 27343
- DOI 10.1209/0295-5075/95/58003
- Persistent URL
- https://folia.unifr.ch/unifr/documents/302172
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