NDLI: The topic-perspective model for social tagging systems

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The topic-perspective model for social tagging systems

Content Provider	ACM Digital Library
Author	Hu, Xiaohua Lu, Caimei Park, Jung-Ran Chen, Xin Li, Zhoujun He, TingTing
Abstract	In this paper, we propose a new probabilistic generative model, called Topic-Perspective Model, for simulating the generation process of social annotations. Different from other generative models, in our model, the tag generation process is separated from the content term generation process. While content terms are only generated from resource topics, social tags are generated by resource topics and user perspectives together. The proposed probabilistic model can produce more useful information than any other models proposed before. The parameters learned from this model include: (1) the topical distribution of each document, (2) the perspective distribution of each user, (3) the word distribution of each topic, (4) the tag distribution of each topic, (5) the tag distribution of each user perspective, (6) and the probabilistic of each tag being generated from resource topics or user perspectives. Experimental results show that the proposed model has better generalization performance or tag prediction ability than other two models proposed in previous research.
Starting Page	683
Ending Page	692
Page Count	10
File Format	PDF QT / MOV
ISBN	9781450300551
DOI	10.1145/1835804.1835891
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-07-25
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social annotation Perplexity Social tagging User modeling
Content Type	Video Text
Resource Type	Article

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