Shang, Ming-Sheng Zhang, Zi-Ke 2009 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. application/pdf https://folia.unifr.ch/global/documents/301467 https://folia.unifr.ch/documents/301467/files/zhang_dbr.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1088/0256-307x/26/11/118903 info:eu-repo/semantics/openAccess License undefined Chinese Physics Letters. - 2009, vol. 26, no. 11, p. 118903 info:eu-repo/classification/udc/53 Diffusion-based recommendation in collaborative tagging systems http://purl.org/coar/resource_type/c_6501