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The topic-perspective model for social tagging systems
Conference proceeding

The topic-perspective model for social tagging systems

Caimei Lu, Xiaohua Hu, Xin Chen, Jung-Ran Park, TingTing He and Zhoujun Li
Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 683-692
25 Jul 2010
DOI: https://doi.org/10.1145/1835804.1835891

Abstract

perplexity social annotation social tagging user modeling
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.

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