The Topic-Perspective Model for Social Tagging Systems

蔡跳

INTRODUCTION

• social data--social annotations--tags

• a new type of information source

• tag recommendation、 prediction 、 clustering、 classification、IR

Tags

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, To

pic-Link LDA

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

DXK doc-topic分布)(w KXW topic-word分布)(t KXT topic-tag分布

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

)(d DXK doc-topic分布)(w KXW topic-word分布)(t KXT topic-tag分布

LXT persp-tag分布

)(u UXL user-persp分布

a vector indicating the probability each tag is generated from topics

Parameter Estimation

• Variational expectation maximization

• Expectation propagation

• Gibbs sampling

Parameter Estimation

Parameter Estimation

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

Experiment Setup

• Topic K Perspective L 的选择

Results

Discovered topics and perspectives

• 谢谢