Tag Correspondence Model for User Tag Suggestion

Tag Correspondence Model for User Tag Suggestion
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用户标签建议的标签对应模型

DOI:
10.1007/s11390-015-1582-6
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发表时间:
2015-09
影响因子:
0.7
通讯作者:
孙茂松
孙茂松
中科院分区:
--
文献类型:
--
作者:
涂存超;刘知远;孙茂松

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一些微博服务鼓励用户使用多个标签来注释自己,表明他们的属性和兴趣。用户标签在个性化推荐和信息检索中起着重要的作用。为了更好地理解用户标签的语义,我们提出了标签对应关系模型(TCM),以识别复杂的标签对应关系,从微博用户的丰富上下文。标签的对应性被称为上下文中与该标签语义相关的唯一元素。在TCM中,我们将微博用户的上下文分为各种来源(如短消息,用户配置文件和邻居)。利用带有注释标签的用户集合,TCM可以自动学习来自多个源的用户标签的对应关系。通过学习对应关系,我们能够解释标签的隐式语义。此外,对于没有标注任何标签的用户,TCM可以根据用户的上下文信息建议标签。在真实数据集上的大量实验表明,我们的方法可以有效地识别标签的对应关系,这可能最终代表标签的语义含义。
Some microblog services encourage users to annotate themselves with multiple tags, indicating their attributes and interests. User tags play an important role for personalized recommendation and information retrieval. In order to better understand the semantics of user tags, we propose Tag Correspondence Model (TCM) to identify complex correspondences of tags from the rich context of microblog users. The correspondence of a tag is referred to as a unique element in the context which is semantically correlated with this tag. In TCM, we divide the context of a microblog user into various sources (such as short messages, user profile, and neighbors). With a collection of users with annotated tags, TCM can automatically learn the correspondences of user tags from multiple sources. With the learned correspondences, we are able to interpret implicit semantics of tags. Moreover, for the users who have not annotated any tags, TCM can suggest tags according to users’ context information. Extensive experiments on a real-world dataset demonstrate that our method can efficiently identify correspondences of tags, which may eventually represent semantic meanings of tags.
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