Learning to Efficiently Rank on Big Data

  • Learning to Efficiently Rank on Big Data
  • Tuesday, 8th, 9:00~12:30, Room 305
  • Presenters: Lidan Wang (Microsoft Bing), Jimmy Lin (University of Maryland),
                      Donald Metzler (Google), Jiawei Han (University of Illinois at Urbana-Champaign)
  • Abstract
    Ranking in response to user queries is a central problem in information retrieval, data mining, and machine learning. In the era of “Big data”, traditional effectiveness-centric ranking techniques tend to get more and more costly (requiring additional hardware and energy costs) to sustain reasonable ranking speed on large data.  The mentality of combating big data by throwing in more hardware/machines will quickly become highly expensive since data is growing at an extremely fast rate oblivious to any cost concerns from us. “Learning to efficiently rank” offers a cost-effective solution to ranking on large data (e.g., billions of documents). That is, it addresses a critically important question — whether it is possible to improve ranking effectiveness on large data without incurring (too much) additional cost?