Dynamic Information Retrieval Modeling

Dynamic Information Retrieval Modeling

Grace Hui Yang, Marc Sloan, Jun Wang
ISBN: 9781627055253 | PDF ISBN: 9781627055260
Copyright © 2016 | 144 Pages | Publication Date: June, 2016

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This is a comprehensive and current introduction to Dynamic Information Retrieval Modeling, the statistical modeling of information retrieval (IR) systems that can adapt to change. It covers techniques ranging from classical 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. Change is at the heart of modern IR systems. Dynamic Information Retrieval Modeling equips you with the tools and knowledge to understand and keep pace with these changes.

The theoretical component of the book is based on the Markov Decision Process, a mathematical framework from the artificial intelligence field that allows for the construction of models that change according to sequential inputs. The book defines the framework and algorithms commonly used, followed by an introduction to theories and algorithms used to incorporate dynamics into an IR model before presenting an array of state-of-the-art research that already does, such as in the areas of session search and online advertising.

Table of Contents

Acknowledgments
Introduction
Information Retrieval Frameworks
Dynamic IR for a Single Query
Dynamic IR for Sessions
Dynamic IR for Recommender Systems
Evaluating Dynamic IR Systems
Conclusion
Bibliography
Authors' Biographies

About the Author(s)

Grace Hui Yang, Georgetown University
Grace Hui Yang is an Assistant Professor in the Department of Computer Science at Georgetown University. Grace's research interests include information retrieval, machine learning, natural language processing and text mining, with the current focus on dynamic search, search engine evaluation, and privacy-preserving information retrieval. Prior to this, she conducted research on question answering, ontology construction, near-duplicate detection, multimedia information retrieval, and opinion and sentiment detection. The results of her research have been published in SIGIR, CIKM, ACL, TREC, ECIR, ICTIR, and WWW since 2002. She was a recipient of the National Science Foundation Faculty Early Career Development (CAREER) Award. Grace co-organized the TREC Dynamic Domain Track and served as area chairs in SIGIR and ACL. She also served in the Information Retrieval Journal Editorial Board.

Marc Sloan, University College London
Marc Sloan has completed a Ph.D. in Information Retrieval at University College London; his thesis was titled Probabilistic Modeling in Dynamic Information Retrieval. His research interests include applying reinforcement learning techniques such as multi-armed bandits and POMDPs to IR learning systems over time, contextual session search and query suggestion. Marc has published and presented IR research in top-tier conferences and journals such as WWW, SIGIR, WSDM, ICTIR and the Information Retrieval Journal. He has interned at Microsoft Research working on contextual, session based search result blending.

Jun Wang, University College London
Jun Wang is a Reader in Computer Science, University College London, and the Founding Director of MSc Web Science and Big Data Analytics. His main research interests are in the areas of information retrieval, data mining and online advertising. He was a recipient of the Beyond Search award sponsored by Microsoft Research, US, in 2007; he also received the Best Doctoral Consortium award in ACM SIGIR06 for his work on collaborative filtering, the Best Paper Prizes in ECIR09 and ECIR12 for information retrieval, and the Best Paper Prize in ADKDD14 for computational advertising. He is also one of the recipients of Yahoo! FREP award 2014. He is an Area Chair of ACM SIGIR05 and has been a Senior PC member of ACM CIKM since 2012.

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