PPT - æ°æ®å·¥ç¨ä¸ç¥è¯å·¥ç¨æè²é¨éç¹å®éªå®¤

More documents

Recommendations

Info

Evaluation on Tweets • Dataset:Tweets2011 – Sample 50 hashtag with clear topic – Extract tweets with these hashtags • Evaluation Metric: H score – IntraDis: average distance between docs under the same hashtag – InterDis: average distance between docs under different hashtags – The smaller H score is, the better topic representation
Evaluation on Baidu Zhidao • Dataset:Baidu Zhidao Q&A – Question classification according to their tags
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 and 10: Topic Modeling meets Big Data
Page 11 and 12: Sparse and Meaningful Topics • L
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: Comparison between different models
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

PPT - æ°æ®å·¥ç¨ä¸ç¥è¯å·¥ç¨æè²é¨éç¹å®éªå®¤

Create successful ePaper yourself

Delete template?

Save as template?

PPT - æ°æ®å·¥ç¨ä¸ç¥è¯å·¥ç¨æè²é¨éç¹å®éªå®¤