Click Models For Web Search
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| Author | : Aleksandr Chuklin |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 117 |
| Release | : 2015-07-01 |
| Genre | : Computers |
| ISBN | : 1627056483 |
With the rapid growth of web search in recent years the problem of modeling its users has started to attract more and more attention of the information retrieval community. This has several motivations. By building a model of user behavior we are essentially developing a better understanding of a user, which ultimately helps us to deliver a better search experience. A model of user behavior can also be used as a predictive device for non-observed items such as document relevance, which makes it useful for improving search result ranking. Finally, in many situations experimenting with real users is just infeasible and hence user simulations based on accurate models play an essential role in understanding the implications of algorithmic changes to search engine results or presentation changes to the search engine result page. In this survey we summarize advances in modeling user click behavior on a web search engine result page. We present simple click models as well as more complex models aimed at capturing non-trivial user behavior patterns on modern search engine result pages. We discuss how these models compare to each other, what challenges they have, and what ways there are to address these challenges. We also study the problem of evaluating click models and discuss the main applications of click models.
| Author | : Aleksandr Chuklin |
| Publisher | : Springer Nature |
| Total Pages | : 99 |
| Release | : 2022-05-31 |
| Genre | : Computers |
| ISBN | : 3031022947 |
With the rapid growth of web search in recent years the problem of modeling its users has started to attract more and more attention of the information retrieval community. This has several motivations. By building a model of user behavior we are essentially developing a better understanding of a user, which ultimately helps us to deliver a better search experience. A model of user behavior can also be used as a predictive device for non-observed items such as document relevance, which makes it useful for improving search result ranking. Finally, in many situations experimenting with real users is just infeasible and hence user simulations based on accurate models play an essential role in understanding the implications of algorithmic changes to search engine results or presentation changes to the search engine result page. In this survey we summarize advances in modeling user click behavior on a web search engine result page. We present simple click models as well as more complex models aimed at capturing non-trivial user behavior patterns on modern search engine result pages. We discuss how these models compare to each other, what challenges they have, and what ways there are to address these challenges. We also study the problem of evaluating click models and discuss the main applications of click models.
| Author | : Christopher D. Manning |
| Publisher | : Cambridge University Press |
| Total Pages | : |
| Release | : 2008-07-07 |
| Genre | : Computers |
| ISBN | : 1139472100 |
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
| Author | : John Berryman |
| Publisher | : Simon and Schuster |
| Total Pages | : 517 |
| Release | : 2016-06-19 |
| Genre | : Computers |
| ISBN | : 1638353611 |
Summary Relevant Search demystifies relevance work. Using Elasticsearch, it teaches you how to return engaging search results to your users, helping you understand and leverage the internals of Lucene-based search engines. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Users are accustomed to and expect instant, relevant search results. To achieve this, you must master the search engine. Yet for many developers, relevance ranking is mysterious or confusing. About the Book Relevant Search demystifies the subject and shows you that a search engine is a programmable relevance framework. You'll learn how to apply Elasticsearch or Solr to your business's unique ranking problems. The book demonstrates how to program relevance and how to incorporate secondary data sources, taxonomies, text analytics, and personalization. In practice, a relevance framework requires softer skills as well, such as collaborating with stakeholders to discover the right relevance requirements for your business. By the end, you'll be able to achieve a virtuous cycle of provable, measurable relevance improvements over a search product's lifetime. What's Inside Techniques for debugging relevance? Applying search engine features to real problems? Using the user interface to guide searchers? A systematic approach to relevance? A business culture focused on improving search About the Reader For developers trying to build smarter search with Elasticsearch or Solr. About the Authors Doug Turnbull is lead relevance consultant at OpenSource Connections, where he frequently speaks and blogs. John Berryman is a data engineer at Eventbrite, where he specializes in recommendations and search. Foreword author, Trey Grainger, is a director of engineering at CareerBuilder and author of Solr in Action. Table of Contents The search relevance problem Search under the hood Debugging your first relevance problem Taming tokens Basic multifield search Term-centric search Shaping the relevance function Providing relevance feedback Designing a relevance-focused search application The relevance-centered enterprise Semantic and personalized search
| Author | : Ian Goodfellow |
| Publisher | : MIT Press |
| Total Pages | : 801 |
| Release | : 2016-11-10 |
| Genre | : Computers |
| ISBN | : 0262337371 |
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
| Author | : Safiya Umoja Noble |
| Publisher | : NYU Press |
| Total Pages | : 245 |
| Release | : 2018-02-20 |
| Genre | : Computers |
| ISBN | : 1479837245 |
Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author
| Author | : Asla Pitkänen |
| Publisher | : Academic Press |
| Total Pages | : 1180 |
| Release | : 2017-06-14 |
| Genre | : Medical |
| ISBN | : 012804067X |
Models of Seizures and Epilepsy, Second Edition, is a valuable, practical reference for investigators who are searching for the most appropriate laboratory models to address key questions in the field. The book also provides an important background for physicians, fellows, and students, offering insight into the potential for advances in epilepsy research as well as R&D drug development. Contents include the current spectrum of models available to model different epilepsy syndromes, epilepsy in transgenic animals, comorbidities in models of epilepsy, and novel technologies to study seizures and epilepsies in animals. - Provides a comprehensive reference detailing animal models of epilepsy and seizure - Offers insights on the use of novel technologies that can be applied in experimental epilepsy research - Edited by leading experts in the field that provide not only technical reviews of these models but also conceptual critiques - Comments on the strengths and limitations of various models, including their relationship to clinical phenomenology and their value in developing better understanding and treatments
| Author | : Marcel Danesi |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 183 |
| Release | : 2012-12-06 |
| Genre | : Education |
| ISBN | : 9401001871 |
This volume offers a practical introduction to the use of neuroscience to teach second languages. It provides information on the relation between how the brain learns and how this can be used to construct classroom activities, evaluates methods, syllabi, approaches, etc. from the perspective of brain functioning. It illustrates how teaching can unfold with actual examples in several languages.
| Author | : Bhaskar Mitra |
| Publisher | : Foundations and Trends (R) in Information Retrieval |
| Total Pages | : 142 |
| Release | : 2018-12-23 |
| Genre | : |
| ISBN | : 9781680835328 |
Efficient Query Processing for Scalable Web Search will be a valuable reference for researchers and developers working on This tutorial provides an accessible, yet comprehensive, overview of the state-of-the-art of Neural Information Retrieval.
| Author | : Shane Parrish |
| Publisher | : Penguin |
| Total Pages | : 209 |
| Release | : 2024-10-15 |
| Genre | : Business & Economics |
| ISBN | : 0593719972 |
Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage.
