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CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY

Diffusion-Based Recommendation in Collaborative Tagging Systems

Shang Ming-Sheng et al 2009 Chinese Phys. Lett. 26 118903 (4pp)   doi: 10.1088/0256-307X/26/11/118903  Help

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Shang Ming-Sheng1 and Zhang Zi-Ke2
1 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054
2 Department of Physics, University of Fribourg, CH-1700 Fribourg, Switzerland
E-mail: msshang@uestc.edu.cn (Shang Ming-Sheng) and zike.zhang@unifr.ch (Zhang Zi-Ke)

Abstract. Recently, collaborative tagging systems have attracted more and more attention and have been widely applied in web systems. Tags provide highly abstracted information about personal preferences and item content, and therefore have the potential to help in improving better personalized recommendations. We propose a diffusion-based recommendation algorithm considering the personal vocabulary and evaluate it in a real-world dataset: Del.icio.us. Experimental results demonstrate that the usage of tag information can significantly improve the accuracy of personalized recommendations.

PACS numbers: 89.75.−k, 89.20.−a, 89.20.Ff

Print publication: Issue 11 (November 2009)
Received 26 August 2009

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