1 / 19

The Topic-Perspective Model: Enhancing Social Tagging Systems through Generative Analytics

This paper explores a novel Topic-Perspective Model for social tagging systems that integrates user, document, word, and tag data within a unified framework. By simulating the annotation generation process, the model addresses both visible and hidden variables, improving tag recommendation, prediction, clustering, and classification tasks. Key methodologies such as Variational Expectation Maximization and Gibbs Sampling are employed to estimate parameters. Experiments conducted on datasets like del.icio.us demonstrate the model's efficacy in enhancing personalized search capabilities and generating relevant tags for new documents.

dea
Télécharger la présentation

The Topic-Perspective Model: Enhancing Social Tagging Systems through Generative Analytics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Topic-Perspective Model for Social Tagging Systems 蔡跳

  2. INTRODUCTION social data--social annotations--tags a new type of information source tag recommendation、prediction 、clustering、classification、IR

  3. Tags

  4. RELATED WORK1 • Topic Analysis using Generative Models text mining: 1.Naïve Bayesian model, 2.Probabilistic Latent Semantic Indexing (PLSI) model, 3.Latent Dirichlet Allocation (LDA) model • correlated LDA, switchLDA, Link-LDA, Topic-Link LDA

  5. RELATED WORK2 • Generative Models for Social Tagging 1.Conditionally-independent LDA (CI-LDA) model 2.Community-based categorical annotation (CCA) model 3.correlated or correspondence LDA (CorrLDA) model

  6. DXK doc-topic分布 KXW topic-word分布 KXT topic-tag分布

  7. Topic-Perspective Model • 真实模拟annotation的生成过程，user 、document、word、tag统一在一个模型中 • motivation：表示和连接可见的及不可见的变量 • Output：user perspective可用于个性化搜素

  8. UXL user-persp分布 DXK doc-topic分布 KXW topic-word分布 KXT topic-tag分布 LXT persp-tag分布 a vector indicating the probability each tag is generated from topics

  9. Parameter Estimation • Variational expectation maximization • Expectation propagation • Gibbs sampling

  10. Parameter Estimation

  11. Parameter Estimation

  12. Experiments and results • Datasets: del.icio.us, 1-2 2009, 41190 documents, 4414 users, 28740 tags, 129908 words, 10% test, 90% train • Evaluation Criterion: perplexity.概括归纳新文档的tags的能力

  13. Experiment Setup • Topic K Perspective L 的选择

  14. Results

  15. Discovered topics and perspectives

  16. 谢谢

More Related