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Understanding the Topic(Topic Sparseness): Group Sparse Topical Coding: From Code to Topic (WSDM’13)
Sparse and Meaningful Topics • Lots of data, Lots of potential topics (Global), but relative sparse topics in individual document (Local)! ! •! Conventional idea:! Doc space – Control the sparseness of the topics at document level! Topic space Probabilistic Model! • Meaningful interpretation! • Hard to control sparse ! Non-probabilistic Model! • Effective sparse controlling! • Lack clear semantic meanings! (Blei et al., ’03) (Lin., ’08) Can we find a better way to enjoy both the merits Yes!!!!
Page 1 and 2: Big Data: Science or Gimmick! 程
Page 3 and 4: Is Big Data Big Value Predicti
Page 5 and 6: Big Challenges Exist in Big Da
Page 7 and 8: Topics in this talk Data Comple
Page 9: Topic Modeling meets Big Data
Page 13 and 14: Group Sparse Topical Coding Group
Page 15 and 16: Results • Datasets - 20-newsgrou
Page 17 and 18: Short texts are prevalent Uncov
Page 19 and 20: Key idea of our approach • T
Page 21 and 22: Comparison between different models
Page 23 and 24: Evaluation on Baidu Zhidao • Dat
Page 25 and 26: Ranking meets Big Data High com
Page 27 and 28: Top-‐k Learning to Rank Ran
Page 29 and 30: Top-‐k Ranking Framework Top
Page 31 and 32: Problems • Predict the percolati
Page 33 and 34: Challenges in the era of Big Data
Page 35 and 36: Can be useful on Social Networ
Page 37 and 38: Case1: Query the Real People o
Page 39 and 40: Summary • Three ScienAfic Chal

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