Dynamic information retrieval modeling

Hui Yang, Marc Sloan, Jun Wang

Abstract: Dynamic aspects of Information Retrieval (IR), including changes found in data, users and systems, are increasingly being utilized in search engines and information filtering systems. Existing IR techniques are limited in their ability to optimize over changes, learn with minimal computational footprint and be responsive and adaptive. The objective of this tutorial is to provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change. It will cover techniques ranging from classic relevance feedback to the latest applications of partially observable Markov decision processes (POMDPs) and a handful of useful algorithms and tools for solving IR problems incorporating dynamics.