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The topic-perspective model for social tagging systems

Published: 25 July 2010 Publication History

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.

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cover image ACM Conferences
KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
July 2010
1240 pages
ISBN:9781450300551
DOI:10.1145/1835804
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 July 2010

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Author Tags

  1. perplexity
  2. social annotation
  3. social tagging
  4. user modeling

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  • (2019)Integrating social annotations into topic models for personalized document retrievalSoft Computing10.1007/s00500-019-03998-1Online publication date: 24-Apr-2019
  • (2017)Exploiting User Consuming Behavior for Effective Item TaggingProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3133071(2175-2178)Online publication date: 6-Nov-2017
  • (2017)MFS-LDA: a multi-feature space tag recommendation model for cold start problemProgram10.1108/PROG-01-2017-000251:3(218-234)Online publication date: 5-Sep-2017
  • (2017)Social Annotation for Query Expansion Learning from Multiple Expansion StrategiesSocial Media Processing10.1007/978-981-10-6805-8_15(181-192)Online publication date: 26-Oct-2017
  • (2016)Pairwise Latent Semantic Association for Similarity Computation in Medical ImagingIEEE Transactions on Biomedical Engineering10.1109/TBME.2015.247802863:5(1058-1069)Online publication date: May-2016
  • (2014)User-Perceptive Multimedia Content AnalysisUser-centric Social Multimedia Computing10.1007/978-3-662-44671-3_2(11-32)Online publication date: 18-Oct-2014
  • (2013)Social Link Prediction in Online Social Tagging SystemsACM Transactions on Information Systems10.1145/251689131:4(1-27)Online publication date: 1-Nov-2013
  • (2013)Exploring generative models of tripartite graphs for recommendation in social mediaProceedings of the 4th International Workshop on Modeling Social Media10.1145/2463656.2463658(1-8)Online publication date: 1-May-2013
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