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Learning-to-Rank for Information Retrieval

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In a world flooded with information, it is becoming increasingly challenging to extract desired information from a large pool of data. Learning-to-Rank (LTR) uses machine learning technologies to solve the problem of information retrieval. One typical usage of LTR is in the ranker of a search engine, which matches processed queries with indexed documents.

In this talk, I will provide an overview of Learning-to-Rank framework, and explain the 3 major approaches: Pointwise, Pairwise, and Listwise; and compare their limitations. I will also elaborate on each approach with an example algorithm developed upon the idea of AdaBoost.

This talk is part of the Churchill CompSci Talks series.

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