The Chinese University of Hong Kong
Department of Computer Science and Engineering


Title: Balancing Trade-offs in Large Scale Multistage Retrieval Systems
Date: July 28, 2017 (Friday)
Time: 10:30 a.m. - 11:30 a.m.
Venue: Room 121, 1/F, Ho Sin-hang Engineering Building,
The Chinese University of Hong Kong,
Shatin, N.T.
Speaker: Dr. J Shane CULPEPPER
Vice-Chancellor's Senior Research Fellow
School of Science (Computer Science)
RMIT University, Australia



In this talk, we will discuss recent work on managing tradeoffs between efficiency and effectiveness in modern multi-stage ranking architectures which are comprised of a candidate generation stage followed by one or more reranking stages. In such an architecture, the quality of the final ranked list may not be sensitive to the quality of initial candidate pool, especially in terms of early precision. We will briefly discuss two recent related papers from my group. In the first work, we explore dynamic cutoff prediction in early stage retrieval using query difficulty pre-retrieval features. We will then turn our attention to efficiency and effectiveness trade-offs in cascaded learning-to-rank algorithms. Specifically, we re-examine the importance of tightly integrating feature costs into multi-stage learning-to-rank (LTR) IR systems, and we present a novel approach to optimizing cascaded ranking models which can directly leverage a variety of different state-of-the-art LTR rankers such as LambdaMART and Gradient Boosted Decision Trees.



Shane Culpepper completed a PhD at The University of Melbourne in 2008. He is currently a Vice-Chancellor's Senior Research Fellow and faculty member at RMIT University where he runs the Information Discovery Research Group. His research focuses on designing and evaluating efficient and effective algorithms and data structures for a wide variety of information storage and retrieval problems. Research interests include information retrieval, text indexing, data compression, system evaluation, information discovery, learning to rank, natural language processing, algorithm engineering, and scalable distributed/parallel computing. For more information about his research, visit his website at


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