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Diffusion-Based Recommendation in Collaborative Tagging Systems

Shang Ming-Sheng1 and Zhang Zi-Ke2

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CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY

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

89.75.Hc Networks and genealogical trees

89.75.Kd Patterns

05.60.-k Transport processes

Subjects

Statistical physics and nonlinear systems

Dates

Issue 11 (November 2009)

Received 26 August 2009



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