| 000 | 07335nam a2200709 i 4500 | ||
|---|---|---|---|
| 001 | 7503459 | ||
| 003 | IEEE | ||
| 005 | 20200413152922.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 160618s2016 caua foab 000 0 eng d | ||
| 020 |
_a9781627055260 _qebook |
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| 020 |
_z9781627055253 _qprint |
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| 024 | 7 |
_a10.2200/S00718ED1V01Y201605ICR049 _2doi |
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| 035 | _a(CaBNVSL)swl00406601 | ||
| 035 | _a(OCoLC)951864853 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aZA3075 _b.Y253 2016 |
|
| 082 | 0 | 4 |
_a028.7071 _223 |
| 100 | 1 |
_aYang, Grace Hui., _eauthor. |
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| 245 | 1 | 0 |
_aDynamic information retrieval modeling / _cGrace Hui Yang, Marc Sloan, Jun Wang. |
| 264 | 1 |
_a[San Rafael, California] : _bMorgan & Claypool, _c2016. |
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| 300 |
_a1 PDF (xvii, 126 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 |
_aSynthesis lectures on information concepts, retrieval, and services, _x1947-9468 ; _v# 49 |
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| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 504 | _aIncludes bibliographical references (pages 101-123). | ||
| 505 | 0 | _a1. Introduction -- 1.1 Dynamics in information retrieval -- 1.2 Challenges -- 1.3 Overview of dynamic IR -- 1.4 Aims of this book -- 1.5 Structure -- | |
| 505 | 8 | _a2. Information retrieval frameworks -- 2.1 Case study: multi-page search -- 2.2 Static information retrievaL -- 2.2.1 The ranking problem -- 2.2.2 The diversification problem -- 2.3 Interactive information retrieval -- 2.3.1 The Rocchio algorithm -- 2.3.2 Interactive probability ranking principle -- 2.4 Dynamic information retrieval -- 2.4.1 Reinforcement learning vs. dynamic IR modeling -- 2.4.2 Markov decision process -- 2.4.3 Partially observable Markov decision process -- 2.4.4 Bandits models -- 2.5 Modeling dynamic IR -- | |
| 505 | 8 | _a3. Dynamic IR for a single query -- 3.1 Information filtering -- 3.1.1 Relevance feedback -- 3.1.2 Active learning -- 3.1.3 Multi-page search -- 3.2 Multi-armed bandits -- 3.2.1 Exploration vs. exploitation -- 3.2.2 Multi-armed bandit variations -- 3.3 Related work -- | |
| 505 | 8 | _a4. Dynamic IR for sessions -- 4.1 Session search -- 4.1.1 Query change: a strong signal from the user -- 4.1.2 Markov chains in sessions -- 4.1.3 Two-way communication in sessions -- 4.2 Modeling sessions in the dynamic IR framework -- 4.2.1 States -- 4.2.2 Actions -- 4.2.3 Rewards -- 4.3 Dual-agent stochastic game: putting users into retrieval models -- 4.3.1 Framework formulation -- 4.3.2 Observation functions -- 4.3.3 Belief updates -- 4.4 Retrieval for sessions -- 4.4.1 Obtaining the policies by heuristics -- 4.4.2 Obtaining the policies by joint optimization -- 4.5 Related work -- | |
| 505 | 8 | _a5. Dynamic IR for recommender systems -- 5.1 Collaborative filtering -- 5.2 Static recommendation -- 5.2.1 User-based approaches -- 5.2.2 Probabilistic matrix factorization -- 5.3 Dynamics in recommendation -- 5.3.1 Objective function -- 5.3.2 User dynamics -- 5.3.3 Item selection via confidence bound -- 5.4 Related work -- | |
| 505 | 8 | _a6. Evaluating dynamic IR systems -- 6.1 IR evaluation -- 6.2 Text retrieval conference (TREC) -- 6.2.1 TREC interactive track -- 6.2.2 TREC session track -- 6.2.3 TREC dynamic domain (DD) track -- 6.3 The water filling model -- 6.4 The cube test -- 6.4.1 Filling up the cube -- 6.4.2 Stopping criteria -- 6.5 Plotting the dynamic progress -- 6.6 Related work -- | |
| 505 | 8 | _a7. Conclusion -- Bibliography -- Authors' biographies. | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aBig 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. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on June 18, 2016). | ||
| 650 | 0 |
_aInformation retrieval _xComputer simulation. |
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| 650 | 0 | _aDynamic programming. | |
| 653 | _adynamic information retrieval | ||
| 653 | _ainformation retrieval models | ||
| 653 | _areinforcement learning | ||
| 653 | _aMarkov decision process | ||
| 653 | _arecommender systems | ||
| 653 | _ainformation retrieval | ||
| 653 | _ainformation retrieval evaluation | ||
| 700 | 1 |
_aSloan, Marc., _eauthor. |
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| 700 | 1 |
_aWang, Jun., _eauthor. |
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| 776 | 0 | 8 |
_iPrint version: _z9781627055253 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on information concepts, retrieval, and services ; _v# 49. _x1947-9468 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7503459 |
| 999 |
_c562217 _d562217 |
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