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Tag Correspondence Model for User Tag Suggestion

涂存超 刘知远 孙茂松

Journal of computer science and technology, 2015-09, Vol.30 (5), p.1063-1072 [Periódico revisado por pares]

New York: Springer US

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  • Título:
    Tag Correspondence Model for User Tag Suggestion
  • Autor: 涂存超 刘知远 孙茂松
  • Assuntos: Annotations ; Artificial Intelligence ; Collaboration ; Collection ; Computer Science ; Computer simulation ; Context ; Correlation ; Correspondence ; Data Structures and Information Theory ; Experiments ; Information retrieval ; Information Systems Applications (incl.Internet) ; Internet ; Laboratories ; Messages ; Microblogs ; Recommender systems ; Regular Paper ; Semantic relations ; Semantics ; Social networks ; Software Engineering ; Startups ; Tagging ; Tags ; Theory of Computation ; 上下文信息 ; 个性化推荐 ; 中医学 ; 标签 ; 标记 ; 模型 ; 用户配置文件 ; 自动学习
  • É parte de: Journal of computer science and technology, 2015-09, Vol.30 (5), p.1063-1072
  • Notas: Cun-Chao Tu, Zhi-Yuan Liu, and Mao-Song Sun（ Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China State Key Laboratory on Intelligent Technology and Systems, Tsinghua University, Beijing 100084, China National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China Jiangsu Collaborative Innovation Center for Language Ability, Jiangsu Normal University, Xuzhou 221009, China）
    11-2296/TP
    microblog； user tag suggestion； tag correspondence model； probabilistic graphical model； context
    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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  • Descrição: 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.
  • Editor: New York: Springer US
  • Idioma: Inglês
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