Dynamic information retrieval modeling (Record no. 56545)

MARC details
000 -LEADER
fixed length control field 05600nam a22002297a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20201028112814.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200703b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781627055253
028 ## - DISTRIBUTOR NAME
Distributor Name Raghav Books Pvt. Ltd.
Distributor address ;A-184 A, Nand Gram Ghaziabad
Bill Number ;1834
Bill Date ;15/06/2020
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 028.7071 YAN
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Grace Hui Yang
-- ; Marc Sloan
-- ; Jun Wang
245 ## - TITLE STATEMENT
Title Dynamic information retrieval modeling
Statement of responsibility, etc /Grace Hui Yang
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc California
Name of publisher, distributor, etc : Morgan & Claypool Publishers
Date of publication, distribution, etc , 2016
300 ## - PHYSICAL DESCRIPTION
Extent xvii, 126 pages
Dimensions ;24 cm.
365 ## - TRADE PRICE
Price amount 5629
490 ## - SERIES STATEMENT
Series statement Synthesis lectures on information concepts, retrieval, and services, #49.
505 ## - FORMATTED CONTENTS NOTE
Contents note 1. Introduction --<br/>1.1 Dynamics in information retrieval --<br/>1.2 Challenges --<br/>1.3 Overview of dynamic IR --<br/>1.4 Aims of this book --<br/>1.5 Structure. 2. Information retrieval frameworks --<br/>2.1 Case study: multi-page search --<br/>2.2 Static information retrievaL --<br/>2.2.1 The ranking problem --<br/>2.2.2 The diversification problem --<br/>2.3 Interactive information retrieval --<br/>2.3.1 The Rocchio algorithm --<br/>2.3.2 Interactive probability ranking principle --<br/>2.4 Dynamic information retrieval --<br/>2.4.1 Reinforcement learning vs. dynamic IR modeling --<br/>2.4.2 Markov decision process --<br/>2.4.3 Partially observable Markov decision process --<br/>2.4.4 Bandits models --<br/>2.5 Modeling dynamic IR. 3. Dynamic IR for a single query --<br/>3.1 Information filtering --<br/>3.1.1 Relevance feedback --<br/>3.1.2 Active learning --<br/>3.1.3 Multi-page search --<br/>3.2 Multi-armed bandits --<br/>3.2.1 Exploration vs. exploitation --<br/>3.2.2 Multi-armed bandit variations --<br/>3.3 Related work. 4. Dynamic IR for sessions --<br/>4.1 Session search --<br/>4.1.1 Query change: a strong signal from the user --<br/>4.1.2 Markov chains in sessions --<br/>4.1.3 Two-way communication in sessions --<br/>4.2 Modeling sessions in the dynamic IR framework --<br/>4.2.1 States --<br/>4.2.2 Actions --<br/>4.2.3 Rewards --<br/>4.3 Dual-agent stochastic game: putting users into retrieval models --<br/>4.3.1 Framework formulation --<br/>4.3.2 Observation functions --<br/>4.3.3 Belief updates --<br/>4.4 Retrieval for sessions --<br/>4.4.1 Obtaining the policies by heuristics --<br/>4.4.2 Obtaining the policies by joint optimization --<br/>4.5 Related work. 5. Dynamic IR for recommender systems --<br/>5.1 Collaborative filtering --<br/>5.2 Static recommendation --<br/>5.2.1 User-based approaches --<br/>5.2.2 Probabilistic matrix factorization --<br/>5.3 Dynamics in recommendation --<br/>5.3.1 Objective function --<br/>5.3.2 User dynamics --<br/>5.3.3 Item selection via confidence bound --<br/>5.4 Related work. 6. Evaluating dynamic IR systems --<br/>6.1 IR evaluation --<br/>6.2 Text retrieval conference (TREC) --<br/>6.2.1 TREC interactive track --<br/>6.2.2 TREC session track --<br/>6.2.3 TREC dynamic domain (DD) track --<br/>6.3 The water filling model --<br/>6.4 The cube test --<br/>6.4.1 Filling up the cube --<br/>6.4.2 Stopping criteria --<br/>6.5 Plotting the dynamic progress --<br/>6.6 Related work. 7. Conclusion --<br/>Bibliography --<br/>Authors' biographies.
520 ## - SUMMARY, ETC.
Summary, etc Big data and human-computer information retrieval (HCIR) are changing IR. They capture the dynamic changes in the data and dynamic interactions of users with IR systems. A dynamic system is one which changes or adapts over time or a sequence of events. Many modern IR systems and data exhibit these characteristics which are largely ignored by conventional techniques. What is missing is an ability for the model to change over time and be responsive to stimulus. Documents, relevance, users and tasks all exhibit dynamic behavior that is captured in data sets typically collected over long time spans and models need to respond to these changes. Additionally, the size of modern datasets enforces limits on the amount of learning a system can achieve. Further to this, advances in IR interface, personalization and ad display demand models that can react to users in real time and in an intelligent, contextual way. In this book we provide a comprehensive and up-to-date introduction to Dynamic Information Retrieval Modeling, the statistical modeling of IR systems that can adapt to change. We define dynamics, what it means within the context of IR and highlight examples of problems where dynamics play an important role. We 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. The theoretical component is based around the Markov Decision Process (MDP), a mathematical framework taken from the field of Artificial Intelligence (AI) that enables us to construct models that change according to sequential inputs. We define the framework and the algorithms commonly used to optimize over it and generalize it to the case where the inputs aren't reliable. We explore the topic of reinforcement learning more broadly and introduce another tool known as a Multi-Armed Bandit which is useful for cases where exploring model parameters is beneficial. Following this we introduce theories and algorithms which can be 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. Change is at the heart of modern Information Retrieval systems and this book will help equip the reader with the tools and knowledge needed to understand Dynamic Information Retrieval Modeling.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Dynamic programming
655 ## - INDEX TERM--GENRE/FORM
Genre/form data or focus term Information retrieval -- Computer simulation.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification   Not For Loan SNU LIBRARY SNU LIBRARY 03/07/2020 Raghav Books Pvt. Ltd. 5629.00   028.7071 YAN 27528 03/07/2020 03/07/2020 Books
    Dewey Decimal Classification     SNU LIBRARY SNU LIBRARY 03/07/2020 Raghav Books Pvt. Ltd. 5629.00   028.7071 YAN 27529 03/07/2020 03/07/2020 Books
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