针对传统人工编辑导致大量类别信息重复和不规范的问题,提出了应用协同过滤技术为中文维基百科文章自动推荐类别.利用中文维基百科中的四个重要语义特征即链入、链出、链入的类别和链出的类别来表示维基百科文章,得到与目标文章相似的前若干篇文章的所有类别后,通过查询返回的相似度值计算各个类别的权重,选择前面的若干个类别作为推荐结果返回给目标文章.实验结果表明了这四个语义特征能较好地表征一篇维基百科文章,同时也验证了协同过滤方法在中文维基百科自动推荐类别中的有效性.%Collaborative filtering was applied to automatically recommend categories for a Chinese Wikipedia article. Four typical semantic features namely incoming link, outgoing link, incoming link categories and outgoing link categories, were adopted to represent articles. Among all the categories of articles similar to target article, several most similar categories were chosen as the recommendation results to the target article, via calculating the similarity value between them. The experimental results show that the four semantic features have efficient performance in Wikipedia article representation. And the collaborative filtering method is also proved to be effective in recommending proper categories for Chinese Wikipedia articles.
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