Method of tags recommendation for blogs: A comparative study

2012 International Conference on Machine Learning and Cybernetics Pub Date : 2012-07-15 DOI:10.1109/ICMLC.2012.6359689

Li-Juan Tang, Cheng-Zhi Zhang

引用次数: 0

Abstract

Tags are products of Web 2.0. They play an important role in user modeling, friends or information recommendation. In this paper, keywords from blogs are extracted by using TextRank and TF*IDF algorithms respectively. The keywords are used to tag recommendation. Experiment results show that the performance of these two algorithms is very closely.

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博客标签推荐方法的比较研究

标签是Web 2.0的产物。它们在用户建模、好友推荐或信息推荐中发挥着重要作用。本文分别使用TextRank和TF*IDF算法提取博客关键词。关键词用于标记推荐。实验结果表明，这两种算法的性能非常接近。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2012 International Conference on Machine Learning and Cybernetics

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