计算机应用研究2012，Vol.29Issue(2)：504-509,6.DOI:10.3969/j.issn.1001-3695.2012.02.028
不同粒度标签推荐算法的比较研究
Comparative research on different grain-based tag recommendation algorithm
摘要
Abstract
Social tagging system has a characteristic that different entities from different grain have different descriptive power. This paper proposed some methods to recommend more precise tags from fine word-grained and coarse topic-grained according to this characteristic. The descriptions and tags of documents were modeled with statistic language model ( fine word-grained) and latent dirichlet allocate model (coarse topic-grained) , respectively. The paper hybrided different single model to recommend tags after using a single model, and then compared their different performances. The results of experiments show that the performance of word-grained tag recommendation is better than the topic-grained one, and the hybrid methods are better than non-hybrid ones, and the less the related features of hybrid are, the better the performance is obtained.关键词
标签推荐/统计语言模型/隐含话题模型/不同粒度Key words
tag recommendation/ statistic language model/ latent dirichlet allocate model/ different grain分类
信息技术与安全科学引用本文复制引用
靳延安,李玉华,刘行军..不同粒度标签推荐算法的比较研究[J].计算机应用研究,2012,29(2):504-509,6.基金项目
国家自然科学基金资助项目(70771043) （70771043）
湖北省教育科学"十一五"规划项目(2010B039) （2010B039）
湖北省人文社科项目(2010Q094) （2010Q094）
