This book presents a systematic study of practices and theories for query understanding of search engines. These studies can be categorized into three major classes. The first class is to figure out what the searcher wants by extracting semantic meaning from the searcher's keywords, such as query classification, query tagging, and query intent understanding. The second class is to analyze search queries and then translate them into an enhanced query that can produce better search results, such as query spelling correction or query rewriting. The third class is to assist users in refining or suggesting queries in order to reduce users' search effort and satisfy their information needs, such as query auto-completion and query suggestion. Query understanding is a fundamental part of search engines. It is responsible to precisely infer the intent of the query formulated by the search user, to correct spelling errors in his/her query, to reformulate the query to capture its intent more accurately, and to guide the user in formulating a query with precise intent. The book will be invaluable to researchers and graduate students in computer or information science and specializing in information retrieval or web-based systems, as well as to researchers and programmers working on the development or improvement of products related to search engines.

This book presents a systematic study of practices and theories for query understanding of search engines. These studies can be categorized into three major classes. The first class is to figure out what the searcher wants by extracting semantic meaning from the searcher's keywords, such as query classification, query tagging, and query intent understanding. The second class is to analyze search queries and then translate them into an enhanced query that can produce better search results, such as query spelling correction or query rewriting. The third class is to assist users in refining or suggesting queries in order to reduce users' search effort and satisfy their information needs, such as query auto-completion and query suggestion. Query understanding is a fundamental part of search engines. It is responsible to precisely infer the intent of the query formulated by the search user, to correct spelling errors in his/her query, to reformulate the query to capture its intent more accurately, and to guide the user in formulating a query with precise intent. The book will be invaluable to researchers and graduate students in computer or information science and specializing in information retrieval or web-based systems, as well as to researchers and programmers working on the development or improvement of products related to search engines.

In plain, uncomplicated language, and using detailed examples to explain the key concepts, models, and algorithms in vertical search ranking, "Relevance Rankingfor Vertical Search Engines" teaches readers how to manipulate ranking algorithms to achieve better results in real-world applications.

This reference book for professionalscovers 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 LabsIntroduces ranking algorithms and teaches readers how to manipulate ranking algorithms for the best resultsCovers concepts and theories from the fundamental to the advancedDiscusses the state of the art: development of theories and practices in vertical search ranking applicationsIncludes detailed examples, case studies and real-world situations"

This book constitutes the refereed proceedings of the 28th China Conference on Information Retrieval, CCIR 2022, held in Chongqing, China, in September 2022. Information retrieval aims to meet the demand of human on the Internet to obtain information quickly and accurately. The 8 full papers presented were carefully reviewed and selected from numerous submissions. The papers provide a wide range of research results in information retrieval area.

This book constitutes the refereed proceedings of the 12th Information Retrieval Societies Conference, AIRS 2016, held in Beijing, China, in November/December 2016. The 21 full papers presented together with 11 short papers were carefully reviewed and selected from 57 submissions. The final programme of AIRS 2015 is divided in the following tracks: IR models and theories; machine learning and data mining for IR; IR applications and user modeling; personalization and recommendation; and IR evaluation.

The COVID-19 pandemic has dealt a severe blow to human capital. This report presents new evidence and analysis to provide a comprehensive diagnostic of the effects of the pandemic on human capital outcomes and identify promising policy responses for governments faced with the task of rebuilding human capital in the wake of the pandemic. The report identifies the mechanisms through which COVID-19 affected the human capital of people at different points in the life cycle and provides estimates of the magnitude of these losses. This analysis underlines differences in impact across countries and groups within countries to understand how the reported blow on human capital has been unequal, exacerbating existing gaps and creating new ones. Grounded in the diagnostic, the report discusses policy responses that attend to afflicted groups in the short-term as well as the medium- to long-term agenda to build back better human capital and make systems more resilient. The long-term policy discussion recognizes COVID-19 as an inflection point, using the opportunity to reimagine systems and institutions, thinking in a completely different way about some key issues. In conclusion, the report reflects on what we have learned from failed policy responses as well as the innovations that proved successful across sectors in preventing or mitigating human capital losses associated with the COVID-19 crisis, and how these lessons can be incorporated across sectors going forward.