Download or read book Learning to Rank for Information Retrieval written by Tie-Yan Liu and published by Springer Science & Business Media. This book was released on 2011-04-29 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”. Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches – these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance. This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.
Download or read book Learning to Rank for Information Retrieval written by Tie-Yan Liu and published by Now Publishers Inc. This book was released on 2009 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning to Rank for Information Retrieval is an introduction to the field of learning to rank, a hot research topic in information retrieval and machine learning. It categorizes the state-of-the-art learning-to-rank algorithms into three approaches from a unified machine learning perspective, describes the loss functions and learning mechanisms in different approaches, reveals their relationships and differences, shows their empirical performances on real IR applications, and discusses their theoretical properties such as generalization ability. As a tutorial, Learning to Rank for Information Retrieval helps people find the answers to the following critical questions: To what respect are learning-to-rank algorithms similar and in which aspects do they differ? What are the strengths and weaknesses of each algorithm? Which learning-to-rank algorithm empirically performs the best? Is ranking a new machine learning problem? What are the unique theoretical issues for ranking as compared to classification and regression? Learning to Rank for Information Retrieval is both a guide for beginners who are embarking on research in this area, and a useful reference for established researchers and practitioners.
Download or read book Learning to Rank for Information Retrieval written by Tie-Yan Liu and published by Springer. This book was released on 2014-09-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as collaborative filtering, definition ranking, question answering, multimedia retrieval, text summarization, and online advertisement. Leveraging machine learning technologies in the ranking process has led to innovative and more effective ranking models, and eventually to a completely new research area called “learning to rank”. Liu first gives a comprehensive review of the major approaches to learning to rank. For each approach he presents the basic framework, with example algorithms, and he discusses its advantages and disadvantages. He continues with some recent advances in learning to rank that cannot be simply categorized into the three major approaches – these include relational ranking, query-dependent ranking, transfer ranking, and semisupervised ranking. His presentation is completed by several examples that apply these technologies to solve real information retrieval problems, and by theoretical discussions on guarantees for ranking performance. This book is written for researchers and graduate students in both information retrieval and machine learning. They will find here the only comprehensive description of the state of the art in a field that has driven the recent advances in search engine development.
Download or read book Learning to Rank for Information Retrieval and Natural Language Processing Second Edition written by Hang Li and published by Springer Nature. This book was released on 2022-05-31 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In ranking creation, given a request, one wants to generate a ranking list of offerings based on the features derived from the request and the offerings. In ranking aggregation, given a request, as well as a number of ranking lists of offerings, one wants to generate a new ranking list of the offerings. Ranking creation (or ranking) is the major problem in learning to rank. It is usually formalized as a supervised learning task. The author gives detailed explanations on learning for ranking creation and ranking aggregation, including training and testing, evaluation, feature creation, and major approaches. Many methods have been proposed for ranking creation. The methods can be categorized as the pointwise, pairwise, and listwise approaches according to the loss functions they employ. They can also be categorized according to the techniques they employ, such as the SVM based, Boosting based, and Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include: PRank, OC SVM, McRank, Ranking SVM, IR SVM, GBRank, RankNet, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, LambdaRank, LambdaMART, Borda Count, Markov Chain, and CRanking. The author explains several example applications of learning to rank including web search, collaborative filtering, definition search, keyphrase extraction, query dependent summarization, and re-ranking in machine translation. A formulation of learning for ranking creation is given in the statistical learning framework. Ongoing and future research directions for learning to rank are also discussed. Table of Contents: Learning to Rank / Learning for Ranking Creation / Learning for Ranking Aggregation / Methods of Learning to Rank / Applications of Learning to Rank / Theory of Learning to Rank / Ongoing and Future Work
Download or read book An Introduction to Neural Information Retrieval written by Bhaskar Mitra and published by Foundations and Trends (R) in Information Retrieval. This book was released on 2018-12-23 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Introduction to Information Retrieval written by Christopher D. Manning and published by Cambridge University Press. This book was released on 2008-07-07 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Statistical Language Models for Information Retrieval written by ChengXiang Zhai and published by Morgan & Claypool Publishers. This book was released on 2009 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: As online information grows dramatically, search engines such as Google are playing a more and more important role in our lives. Critical to all search engines is the problem of designing an effective retrieval model that can rank documents accurately for a given query. This has been a central research problem in information retrieval for several decades. In the past ten years, a new generation of retrieval models, often referred to as statistical language models, has been successfully applied to solve many different information retrieval problems. Compared with the traditional models such as the vector space model, these new models have a more sound statistical foundation and can leverage statistical estimation to optimize retrieval parameters. They can also be more easily adapted to model non-traditional and complex retrieval problems. Empirically, they tend to achieve comparable or better performance than a traditional model with less effort on parameter tuning. This book systematically reviews the large body of literature on applying statistical language models to information retrieval with an emphasis on the underlying principles, empirically effective language models, and language models developed for non-traditional retrieval tasks. All the relevant literature has been synthesized to make it easy for a reader to digest the research progress achieved so far and see the frontier of research in this area. The book also offers practitioners an informative introduction to a set of practically useful language models that can effectively solve a variety of retrieval problems. No prior knowledge about information retrieval is required, but some basic knowledge about probability and statistics would be useful for fully digesting all the details. Table of Contents: Introduction / Overview of Information Retrieval Models / Simple Query Likelihood Retrieval Model / Complex Query Likelihood Model / Probabilistic Distance Retrieval Model / Language Models for Special Retrieval Tasks / Language Models for Latent Topic Analysis / Conclusions
Download or read book Advances in Information Retrieval written by Djoerd Hiemstra and published by Springer Nature. This book was released on 2021-03-26 with total page 808 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 12656 and 12657 constitutes the refereed proceedings of the 43rd European Conference on IR Research, ECIR 2021, held virtually in March/April 2021, due to the COVID-19 pandemic. The 50 full papers presented together with 11 reproducibility papers, 39 short papers, 15 demonstration papers, 12 CLEF lab descriptions papers, 5 doctoral consortium papers, 5 workshop abstracts, and 8 tutorials abstracts were carefully reviewed and selected from 436 submissions. The accepted contributions cover the state of the art in IR: deep learning-based information retrieval techniques, use of entities and knowledge graphs, recommender systems, retrieval methods, information extraction, question answering, topic and prediction models, multimedia retrieval, and much more.
Download or read book Preference Learning written by Johannes Fürnkranz and published by Springer Science & Business Media. This book was released on 2010-11-19 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.
Download or read book Genetic Programming IV written by John R. Koza and published by Springer Science & Business Media. This book was released on 2005-03-21 with total page 626 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Programming IV: Routine Human-Competitive Machine Intelligence presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks, and metabolic pathways. The book describes fifteen instances where GP has created an entity that either infringes or duplicates the functionality of a previously patented 20th-century invention, six instances where it has done the same with respect to post-2000 patented inventions, two instances where GP has created a patentable new invention, and thirteen other human-competitive results. The book additionally establishes: GP now delivers routine human-competitive machine intelligence GP is an automated invention machine GP can create general solutions to problems in the form of parameterized topologies GP has delivered qualitatively more substantial results in synchrony with the relentless iteration of Moore's Law
Download or read book Relevance Ranking for Vertical Search Engines written by Bo Long and published by Newnes. This book was released on 2014-01-25 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: In plain, uncomplicated language, and using detailed examples to explain the key concepts, models, and algorithms in vertical search ranking, Relevance Ranking for Vertical Search Engines teaches readers how to manipulate ranking algorithms to achieve better results in real-world applications. This reference book for professionals covers concepts and theories from the fundamental to the advanced, such as relevance, query intention, location-based relevance ranking, and cross-property ranking. It covers the most recent developments in vertical search ranking applications, such as freshness-based relevance theory for new search applications, location-based relevance theory for local search applications, and cross-property ranking theory for applications involving multiple verticals. Foreword by Ron Brachman, Chief Scientist and Head, Yahoo! Labs Introduces ranking algorithms and teaches readers how to manipulate ranking algorithms for the best results Covers concepts and theories from the fundamental to the advanced Discusses the state of the art: development of theories and practices in vertical search ranking applications Includes detailed examples, case studies and real-world situations
Download or read book Information Retrieval written by Stefan Buttcher and published by MIT Press. This book was released on 2016-02-12 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to information retrieval, the foundation for modern search engines, that emphasizes implementation and experimentation. Information retrieval is the foundation for modern search engines. This textbook offers an introduction to the core topics underlying modern search technologies, including algorithms, data structures, indexing, retrieval, and evaluation. The emphasis is on implementation and experimentation; each chapter includes exercises and suggestions for student projects. Wumpus—a multiuser open-source information retrieval system developed by one of the authors and available online—provides model implementations and a basis for student work. The modular structure of the book allows instructors to use it in a variety of graduate-level courses, including courses taught from a database systems perspective, traditional information retrieval courses with a focus on IR theory, and courses covering the basics of Web retrieval. In addition to its classroom use, Information Retrieval will be a valuable reference for professionals in computer science, computer engineering, and software engineering.
Download or read book Modern Information Retrieval written by Yates and published by Pearson Education India. This book was released on 1999-09 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mobile Information Retrieval written by Fabio Crestani and published by Springer. This book was released on 2017-07-19 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a helpful starting point in the scattered, rich, and complex body of literature on Mobile Information Retrieval (Mobile IR), reviewing more than 200 papers in nine chapters. Highlighting the most interesting and influential contributions that have appeared in recent years, it particularly focuses on both user interaction and techniques for the perception and use of context, which, taken together, shape much of today’s research on Mobile IR. The book starts by addressing the differences between IR and Mobile IR, while also reviewing the foundations of Mobile IR research. It then examines the different kinds of documents, users, and information needs that can be found in Mobile IR, and which set it apart from standard IR. Next, it discusses the two important issues of user interfaces and context-awareness. In closing, it covers issues related to the evaluation of Mobile IR applications. Overall, the book offers a valuable tool, helping new and veteran researchers alike to navigate this exciting and highly dynamic area of research.
Download or read book A Survey of Query Auto Completion in Information Retrieval written by Fei Cai and published by . This book was released on 2016-09-13 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: In information retrieval, query auto completion (QAC), also known as type-ahead and auto-complete suggestion, helps users to formulate their query when they have an intent in mind but not a clear way of expressing this in a query. This monograph surveys this research.
Download or read book SIGIR 94 written by W. Bruce Croft and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Information retrieval (IR) is becoming an increasingly important area as scientific, business and government organisations take up the notion of "information superhighways" and make available their full text databases for searching. Containing a selection of 35 papers taken from the 17th Annual SIGIR Conference held in Dublin, Ireland in July 1994, the book addresses basic research and provides an evaluation of information retrieval techniques in applications. Topics covered include text categorisation, indexing, user modelling, IR theory and logic, natural language processing, statistical and probabilistic models of information retrieval systems, routing, passage retrieval, and implementation issues.
Download or read book Search Engines written by Bruce Croft and published by Pearson Higher Ed. This book was released on 2011-11-21 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Search Engines: Information Retrieval in Practice is ideal for introductory information retrieval courses at the undergraduate and graduate level in computer science, information science and computer engineering departments. It is also a valuable tool for search engine and information retrieval professionals. Written by a leader in the field of information retrieval, Search Engines: Information Retrieval in Practice , is designed to give undergraduate students the understanding and tools they need to evaluate, compare and modify search engines. Coverage of the underlying IR and mathematical models reinforce key concepts. The book’s numerous programming exercises make extensive use of Galago, a Java-based open source search engine.
