The Topic-Perspective Model for Social Tagging Systems

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

15. Results

16. Discovered topics and perspectives

19. 谢谢