Dynamic Information Retrieval Modeling PDF Download

Author: Grace Hui Yang
Publisher: Springer Nature
ISBN: 3031023013
Category : Computers
Languages : en
Pages : 126

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Book Description
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.

Author: Donald H. Kraft
Publisher: Springer Nature
ISBN: 3031023072
Category : Computers
Languages : en
Pages : 63

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Book Description
Information retrieval used to mean looking through thousands of strings of texts to find words or symbols that matched a user's query. Today, there are many models that help index and search more effectively so retrieval takes a lot less time. Information retrieval (IR) is often seen as a subfield of computer science and shares some modeling, applications, storage applications and techniques, as do other disciplines like artificial intelligence, database management, and parallel computing. This book introduces the topic of IR and how it differs from other computer science disciplines. A discussion of the history of modern IR is briefly presented, and the notation of IR as used in this book is defined. The complex notation of relevance is discussed. Some applications of IR is noted as well since IR has many practical uses today. Using information retrieval with fuzzy logic to search for software terms can help find software components and ultimately help increase the reuse of software. This is just one practical application of IR that is covered in this book. Some of the classical models of IR is presented as a contrast to extending the Boolean model. This includes a brief mention of the source of weights for the various models. In a typical retrieval environment, answers are either yes or no, i.e., on or off. On the other hand, fuzzy logic can bring in a "degree of" match, vs. a crisp, i.e., strict match. This, too, is looked at and explored in much detail, showing how it can be applied to information retrieval. Fuzzy logic is often times considered a soft computing application and this book explores how IR with fuzzy logic and its membership functions as weights can help indexing, querying, and matching. Since fuzzy set theory and logic is explored in IR systems, the explanation of where the fuzz is ensues. The concept of relevance feedback, including pseudorelevance feedback is explored for the various models of IR. For the extended Boolean model, the use of genetic algorithms for relevance feedback is delved into. The concept of query expansion is explored using rough set theory. Various term relationships is modeled and presented, and the model extended for fuzzy retrieval. An example using the UMLS terms is also presented. The model is also extended for term relationships beyond synonyms. Finally, this book looks at clustering, both crisp and fuzzy, to see how that can improve retrieval performance. An example is presented to illustrate the concepts.

Author: Robert M. Losee
Publisher: Springer Nature
ISBN: 303102317X
Category : Computers
Languages : en
Pages : 59

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Book Description
Information Retrieval performance measures are usually retrospective in nature, representing the effectiveness of an experimental process. However, in the sciences, phenomena may be predicted, given parameter values of the system. After developing a measure that can be applied retrospectively or can be predicted, performance of a system using a single term can be predicted given several different types of probabilistic distributions. Information Retrieval performance can be predicted with multiple terms, where statistical dependence between terms exists and is understood. These predictive models may be applied to realistic problems, and then the results may be used to validate the accuracy of the methods used. The application of metadata or index labels can be used to determine whether or not these features should be used in particular cases. Linguistic information, such as part-of-speech tag information, can increase the discrimination value of existing terminology and can be studied predictively. This work provides methods for measuring performance that may be used predictively. Means of predicting these performance measures are provided, both for the simple case of a single term in the query and for multiple terms. Methods of applying these formulae are also suggested.

Author: Xie, Iris
Publisher: IGI Global
ISBN: 1599042428
Category : Computers
Languages : en
Pages : 376

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Book Description
"This book includes the integration of existing frameworks on user-oriented information retrieval systems across multiple disciplines; the comprehensive review of empirical studies of interactive information retrieval systems for different types of users, tasks, and subtasks; and the discussion of how to evaluate interactive information retrieval systems. "--Provided by publisher.

Author: David Hawking
Publisher: Springer Nature
ISBN: 3031023234
Category : Computers
Languages : en
Pages : 162

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Book Description
Simulated test collections may find application in situations where real datasets cannot easily be accessed due to confidentiality concerns or practical inconvenience. They can potentially support Information Retrieval (IR) experimentation, tuning, validation, performance prediction, and hardware sizing. Naturally, the accuracy and usefulness of results obtained from a simulation depend upon the fidelity and generality of the models which underpin it. The fidelity of emulation of a real corpus is likely to be limited by the requirement that confidential information in the real corpus should not be able to be extracted from the emulated version. We present a range of methods exploring trade-offs between emulation fidelity and degree of preservation of privacy. We present three different simple types of text generator which work at a micro level: Markov models, neural net models, and substitution ciphers. We also describe macro level methods where we can engineer macro properties of a corpus, giving a range of models for each of the salient properties: document length distribution, word frequency distribution (for independent and non-independent cases), word length and textual representation, and corpus growth. We present results of emulating existing corpora and for scaling up corpora by two orders of magnitude. We show that simulated collections generated with relatively simple methods are suitable for some purposes and can be generated very quickly. Indeed it may sometimes be feasible to embed a simple lightweight corpus generator into an indexer for the purpose of efficiency studies. Naturally, a corpus of artificial text cannot support IR experimentation in the absence of a set of compatible queries. We discuss and experiment with published methods for query generation and query log emulation. We present a proof-of-the-pudding study in which we observe the predictive accuracy of efficiency and effectiveness results obtained on emulated versions of TREC corpora. The study includes three open-source retrieval systems and several TREC datasets. There is a trade-off between confidentiality and prediction accuracy and there are interesting interactions between retrieval systems and datasets. Our tentative conclusion is that there are emulation methods which achieve useful prediction accuracy while providing a level of confidentiality adequate for many applications. Many of the methods described here have been implemented in the open source project SynthaCorpus, accessible at: https://bitbucket.org/davidhawking/synthacorpus/

Author: Archana Patel
Publisher: CRC Press
ISBN: 100088127X
Category : Computers
Languages : en
Pages : 246

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Book Description
Gone are the days when data was interlinked with related data by humans and human interpretation was required. Data is no longer just data. It is now considered a Thing or Entity or Concept with meaning, so that a machine not only understands the concept but also extrapolates the way humans do. Data Science with Semantic Technologies: Deployment and Exploration, the second volume of a two-volume handbook set, provides a roadmap for the deployment of semantic technologies in the field of data science and enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book offers the answer to various questions like: What makes a technology semantic as opposed to other approaches to data science? What is knowledge data science? How does knowledge data science relate to other fields? This book explores the optimal use of these technologies to provide the highest benefit to the user under one comprehensive source and title. As there is no dedicated book available in the market on this topic at this time, this book becomes a unique resource for scholars, researchers, data scientists, professionals, and practitioners. This volume can serve as an important guide toward applications of data science with semantic technologies for the upcoming generation.

Author: Anthony Anjorin
Publisher: Springer
ISBN: 3319614827
Category : Computers
Languages : en
Pages : 317

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Book Description
This book constitutes the proceedings of the 13th European Conference on Modelling Foundations and Applications, ECMFA 2017, held as part of STAF 2017, in Marburg, Germany, in July 2017. The 18 papers presented in this volume were carefully reviewed and selected from 48 submissions. The papers are organized in the following topical sections: meta-modeling and language engineering; model evolution and maintenance; model-driven generative development; model consistency management; model verification and analysis; and experience reports, case studies and new applications scenarios.

Author: Jouis, Christophe
Publisher: IGI Global
ISBN: 1466603313
Category : Computers
Languages : en
Pages : 560

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Book Description
Recent technological progress in computer science, Web technologies, and the constantly evolving information available on the Internet has drastically changed the landscape of search and access to information. Current search engines employ advanced techniques involving machine learning, social networks, and semantic analysis. Next Generation Search Engines: Advanced Models for Information Retrieval is intended for scientists and decision-makers who wish to gain working knowledge about search in order to evaluate available solutions and to dialogue with software and data providers. The book aims to provide readers with a better idea of the new trends in applied research.

Author: Tefko Saracevic
Publisher: Springer Nature
ISBN: 3031023021
Category : Computers
Languages : en
Pages : 109

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Book Description
Everybody knows what relevance is. It is a "ya'know" notion, concept, idea–no need to explain whatsoever. Searching for relevant information using information technology (IT) became a ubiquitous activity in contemporary information society. Relevant information means information that pertains to the matter or problem at hand—it is directly connected with effective communication. The purpose of this book is to trace the evolution and with it the history of thinking and research on relevance in information science and related fields from the human point of view. The objective is to synthesize what we have learned about relevance in several decades of investigation about the notion in information science. This book deals with how people deal with relevance—it does not cover how systems deal with relevance; it does not deal with algorithms. Spurred by advances in information retrieval (IR) and information systems of various kinds in handling of relevance, a number of basic questions are raised: But what is relevance to start with? What are some of its properties and manifestations? How do people treat relevance? What affects relevance assessments? What are the effects of inconsistent human relevance judgments on tests of relative performance of different IR algorithms or approaches? These general questions are discussed in detail.

Author: Wei Ding
Publisher: Springer Nature
ISBN: 3031023080
Category : Computers
Languages : en
Pages : 164

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Book Description
Information Architecture is about organizing and simplifying information, designing and integrating information spaces/systems, and creating ways for people to find and interact with information content. Its goal is to help people understand and manage information and make the right decisions accordingly. This updated and revised edition of the book looks at integrated information spaces in the web context and beyond, with a focus on putting theories and principles into practice. In the ever-changing social, organizational, and technological contexts, information architects not only design individual information spaces (e.g., websites, software applications, and mobile devices), but also tackle strategic aggregation and integration of multiple information spaces across websites, channels, modalities, and platforms. Not only do they create predetermined navigation pathways, but they also provide tools and rules for people to organize information on their own and get connected with others. Information architects work with multi-disciplinary teams to determine the user experience strategy based on user needs and business goals, and make sure the strategy gets carried out by following the user-centered design (UCD) process via close collaboration with others. Drawing on the authors’ extensive experience as HCI researchers, User Experience Design practitioners, and Information Architecture instructors, this book provides a balanced view of the IA discipline by applying theories, design principles, and guidelines to IA and UX practices. It also covers advanced topics such as iterative design, UX decision support, and global and mobile IA considerations. Major revisions include moving away from a web-centric view toward multi-channel, multi-device experiences. Concepts such as responsive design, emerging design principles, and user-centered methods such as Agile, Lean UX, and Design Thinking are discussed and related to IA processes and practices.