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In Dynamic Information Retrieval modeling we model dynamic systems which change or adapt over time or a sequence of events using a range of techniques from artificial intelligence and reinforcement learning. Many of the open problems in current IR research can be described as dynamic systems, for instance, session search or computational advertising. State of the art research provides solutions to these
problems that are responsive to a changing environment, learn from past interactions and predict future utility. Advances in IR interface, personalization and ad display demand models that can react to users in real time and in an intelligent, contextual way.
The objective of this tutorial is to provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling. We motivate a conceptual model linking static, interactive and dynamic retrieval and use this to define dynamics within the context of IR. We then cover a number of algorithms and techniques from the
artificial intelligence (AI) and online learning literature such as Markov Decision Processes (MDP), their partially observable variation (POMDP) and multi-armed bandits. Following this we describe how to identify dynamics in an IR problem and demonstrate how to model them using the described techniques. The remainder of the tutorial will then cover an array of state-of-the-art research on dynamic systems in IR and how they can be modeled using dynamic IR. We use research on session search, multi-page search and online advertising as in-depth examples of such work.
An older version of this tutorial presented at SIGIR 2014 can be found here http://www.slideshare.net/marcCsloan/dynamic-information-retrieval-tutorial
This version has a greater emphasis on the underlying theory and a guest lecture on evaluation by Dr Emine Yilmaz. This newer version presents a wider range of applications of DIR in state of the art research and includes a guest lecture on evaluation by Prof Charles Clarke.
http://www.dynamic-ir-modeling.org/
@inproceedings{Yang:2015:DIR:2684822.2697038,
author = {Yang, Hui and Sloan, Marc and Wang, Jun},
title = {Dynamic Information Retrieval Modeling},
booktitle = {Proceedings of the Eighth ACM International Conference on Web Search and Data Mining},
series = {WSDM '15},
year = {2015},
isbn = {978-1-4503-3317-7},
location = {Shanghai, China},
pages = {409--410},
numpages = {2},
url = {http://doi.acm.org/10.1145/2684822.2697038},
doi = {10.1145/2684822.2697038},
acmid = {2697038},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {dynamic information retrieval modeling, probabilistic relevance model, reinforcement learning},
}