摘要
标签作为用户生成的对资源的描述,反映了资源的语义和用户的兴趣。由于Web资源的动态性,标签数据相应地表现出较为明显的时态特征,已有相关研究中标签的时态特征却很少受到关注。针对这方面的不足,对标签数据的时态特征以及基于时态特征的标签间语义关联进行分析,并提出发现标签时态特征的时间段划分准则;为了评价标签时态特征的价值,以经典的统计主题模型为基础,提出新的模型用于分析数据时态特征对所生成主题的影响,并将其用于标签预测。在多个数据集上的测试验证了标签数据的时态特性及其对提高标签预测性能的影响。
Tag is a kind of description of Web resources generated by users,and it represents the semantics of resources and interests of users.Because the Web resources are dynamic,tags show some temporal features.However,few researches are concentrated on temporal features of tags.The temporal features represented by tags dataset were analyzed in this paper,and the semantic relations between tags based temporal features were discussed.The principle of time segmentation for discovering temporal features was proposed,and the effect of tags temporal on topics was analyzed by statistical topic model.The discovered temporal features were used in tags predication.The experiments based on different datasets shows that applying tags temporal feature to tags predication can improve the predication performance.
出处
《计算机科学》
CSCD
北大核心
2012年第6期179-183,共5页
Computer Science
基金
国家自然科学基金项目(61003061)资助
关键词
标签
语义关联
时态
统计主题模型
Tags
Semantic relation
Temporal
Statistical topic model
