Learning To Rank For Information Retrieval

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Learning to Rank for Information Retrieval

Tie-Yan Liu
Learning to Rank for Information Retrieval Book PDF

Author : Tie-Yan Liu
Publisher : Springer Science & Business Media
Page : 282 pages
File Size : 42,7 Mb
Release : 2011-04-29
Category : Computers
ISBN : 9783642142673

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Learning to Rank for Information Retrieval by Tie-Yan Liu Pdf

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.

Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition

Hang Li
Learning to Rank for Information Retrieval and Natural Language Processing Second Edition Book PDF

Author : Hang Li
Publisher : Springer Nature
Page : 107 pages
File Size : 40,5 Mb
Release : 2022-05-31
Category : Computers
ISBN : 9783031021558

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Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition by Hang Li Pdf

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

An Introduction to Neural Information Retrieval

Bhaskar Mitra,Nick Craswell
An Introduction to Neural Information Retrieval Book PDF

Author : Bhaskar Mitra,Nick Craswell
Publisher : Foundations and Trends (R) in Information Retrieval
Page : 142 pages
File Size : 54,6 Mb
Release : 2018-12-23
Category : Electronic
ISBN : 1680835327

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An Introduction to Neural Information Retrieval by Bhaskar Mitra,Nick Craswell Pdf

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.

Statistical Language Models for Information Retrieval

ChengXiang Zhai
Statistical Language Models for Information Retrieval Book PDF

Author : ChengXiang Zhai
Publisher : Morgan & Claypool Publishers
Page : 142 pages
File Size : 54,6 Mb
Release : 2009
Category : Computers
ISBN : 9781598295900

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Statistical Language Models for Information Retrieval by ChengXiang Zhai Pdf

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

Introduction to Information Retrieval

Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze
Introduction to Information Retrieval Book PDF

Author : Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze
Publisher : Cambridge University Press
Page : 128 pages
File Size : 42,6 Mb
Release : 2008-07-07
Category : Computers
ISBN : 9781139472104

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Introduction to Information Retrieval by Christopher D. Manning,Prabhakar Raghavan,Hinrich Schütze Pdf

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.

Machine Learning: ECML 2007

Joost N. Kok,Jacek Koronacki,Ramon Lopez de Mantaras,Stan Matwin,Dunja Mladenic
Machine Learning ECML 2007 Book PDF

Author : Joost N. Kok,Jacek Koronacki,Ramon Lopez de Mantaras,Stan Matwin,Dunja Mladenic
Publisher : Springer
Page : 812 pages
File Size : 43,9 Mb
Release : 2007-09-08
Category : Computers
ISBN : 9783540749585

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Machine Learning: ECML 2007 by Joost N. Kok,Jacek Koronacki,Ramon Lopez de Mantaras,Stan Matwin,Dunja Mladenic Pdf

This book constitutes the refereed proceedings of the 18th European Conference on Machine Learning, ECML 2007, held in Warsaw, Poland, September 2007, jointly with PKDD 2007. The 41 revised full papers and 37 revised short papers presented together with abstracts of four invited talks were carefully reviewed and selected from 592 abstracts submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.

Preference Learning

Johannes Fürnkranz,Eyke Hüllermeier
Preference Learning Book PDF

Author : Johannes Fürnkranz,Eyke Hüllermeier
Publisher : Springer Science & Business Media
Page : 457 pages
File Size : 47,9 Mb
Release : 2010-11-19
Category : Computers
ISBN : 9783642141256

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Preference Learning by Johannes Fürnkranz,Eyke Hüllermeier Pdf

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.

Learning to Rank for Information Retrieval and Natural Language Processing

Hang Li
Learning to Rank for Information Retrieval and Natural Language Processing Book PDF

Author : Hang Li
Publisher : Springer Nature
Page : 107 pages
File Size : 49,6 Mb
Release : 2011-04-20
Category : Computers
ISBN : 9783031021411

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Learning to Rank for Information Retrieval and Natural Language Processing by Hang Li Pdf

Learning to rank refers to machine learning techniques for training the 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 the problem recently and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, existing 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 SVM, Neural Network based approaches. The author also introduces some popular learning to rank methods in details. These include PRank, OC SVM, Ranking SVM, IR SVM, GBRank, RankNet, LambdaRank, ListNet & ListMLE, AdaRank, SVM MAP, SoftRank, 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: Introduction / 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

Relevance Ranking for Vertical Search Engines

Bo Long,Yi Chang
Relevance Ranking for Vertical Search Engines Book PDF

Author : Bo Long,Yi Chang
Publisher : Newnes
Page : 264 pages
File Size : 40,5 Mb
Release : 2014-01-25
Category : Computers
ISBN : 9780124072022

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Relevance Ranking for Vertical Search Engines by Bo Long,Yi Chang Pdf

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

Advances in Information Retrieval

Djoerd Hiemstra,Marie-Francine Moens,Josiane Mothe,Raffaele Perego,Martin Potthast,Fabrizio Sebastiani
Advances in Information Retrieval Book PDF

Author : Djoerd Hiemstra,Marie-Francine Moens,Josiane Mothe,Raffaele Perego,Martin Potthast,Fabrizio Sebastiani
Publisher : Springer Nature
Page : 808 pages
File Size : 42,7 Mb
Release : 2021-03-26
Category : Computers
ISBN : 9783030721138

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Advances in Information Retrieval by Djoerd Hiemstra,Marie-Francine Moens,Josiane Mothe,Raffaele Perego,Martin Potthast,Fabrizio Sebastiani Pdf

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.

Information Retrieval

Stefan Buttcher,Charles L. A. Clarke,Gordon V. Cormack
Information Retrieval Book PDF

Author : Stefan Buttcher,Charles L. A. Clarke,Gordon V. Cormack
Publisher : MIT Press
Page : 633 pages
File Size : 45,5 Mb
Release : 2016-02-12
Category : Computers
ISBN : 9780262528870

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Information Retrieval by Stefan Buttcher,Charles L. A. Clarke,Gordon V. Cormack Pdf

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.

Advances in Knowledge Discovery and Data Mining

Thanaruk Theeramunkong,Boonserm Kijsirikul,Nick Cercone,Tu-Bao Ho
Advances in Knowledge Discovery and Data Mining Book PDF

Author : Thanaruk Theeramunkong,Boonserm Kijsirikul,Nick Cercone,Tu-Bao Ho
Publisher : Springer
Page : 1076 pages
File Size : 41,9 Mb
Release : 2009-04-21
Category : Computers
ISBN : 9783642013072

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Advances in Knowledge Discovery and Data Mining by Thanaruk Theeramunkong,Boonserm Kijsirikul,Nick Cercone,Tu-Bao Ho Pdf

This book constitutes the refereed proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009, held in Bangkok, Thailand, in April 2009. The 39 revised full papers and 73 revised short papers presented together with 3 keynote talks were carefully reviewed and selected from 338 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scientific discovery, data visualization, causal induction, and knowledge-based systems.

SIGIR ’94

W. Bruce Croft,C.J. van Rijsbergen
SIGIR 94 Book PDF

Author : W. Bruce Croft,C.J. van Rijsbergen
Publisher : Springer Science & Business Media
Page : 371 pages
File Size : 44,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781447120995

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SIGIR ’94 by W. Bruce Croft,C.J. van Rijsbergen Pdf

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.

Information Retrieval

Qi Zhang,Xiangwen Liao,Zhaochun Ren
Information Retrieval Book PDF

Author : Qi Zhang,Xiangwen Liao,Zhaochun Ren
Publisher : Springer Nature
Page : 173 pages
File Size : 53,7 Mb
Release : 2019-09-18
Category : Computers
ISBN : 9783030316242

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Information Retrieval by Qi Zhang,Xiangwen Liao,Zhaochun Ren Pdf

This book constitutes the refereed proceedings of the 25th China Conference on Information Retrieval, CCIR 2019, held in Fuzhou, China, in September 2019. The 13 full papers presented were carefully reviewed and selected from 45 submissions. Academic research is aimed at the precise acquisition of information and knowledge by human beings. The research results will support national strategic decision-making, promote the development of the Internet and IT fields, enhance the production efficiency of the industry, and have a major impact on various fields of social life.