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Ranking (Algorithm): Top-‐k Learning to Rank: Labeling, Ranking and Evaluaon (SIGIR’12)
Top-‐k Learning to Rank Ranking with Big Data WSDM n is usually 10 5 ~10 6 Multi-‐level ratings 1 2 3 4 Global learning Full-‐order groundtruth Top-‐K groundtruth 1 2 3 4 5 ………………………………………… n 1 2 3 4 … k Global prediction Local learning Users mainly care about top-‐k ranking, k is usually 10-‐20
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 and 22: Comparison between different models
Page 23 and 24: Evaluation on Baidu Zhidao • Dat
Page 25: Ranking meets Big Data High com
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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