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A Survey of Query Auto Completion in Information Retrieval



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In information retrieval, query auto completion (QAC), also known as type-ahead and auto-complete suggestion, refers to the following functionality: given a prefix consisting of a number of characters entered into a search box, the user interface proposes alternative ways of extending the prefix to a full query. QAC helps users to formulate their query when they have an intent in mind but not a clear way of expressing this in a query. It helps to avoid possible spelling mistakes, especially on devices with small screens. It saves keystrokes and cuts down the search duration of users which implies a lower load on the search engine, and results in savings in machine resources and maintenance. Because of the clear benefits of QAC, a considerable number of algorithmic approaches to QAC have been proposed in the past few years. Query logs have proven to be a key asset underlying most of the recent research. This monograph surveys this research. It focuses on summarizing the literature on QAC and provides a general understanding of the wealth of QAC approaches that are currently available. A Survey of Query Auto Completion in Information Retrieval is an ideal reference on the topic. Its contributions can be summarized as follows: It provides researchers who are working on query auto completion or related problems in the field of information retrieval with a good overview and analysis of state-of-the-art QAC approaches. In particular, for researchers new to the field, the survey can serve as an introduction to the state-of-the-art. It also offers a comprehensive perspective on QAC approaches by presenting a taxonomy of existing solutions. In addition, it presents solutions for QAC under different conditions such as available high-resolution query logs, in-depth user interactions with QAC using eye-tracking, and elaborate user engagements in a QAC process. It also discusses practical issues related to QAC. Lastly, it presents a detailed discussion of core challenges and promising open directions in QAC.






In information retrieval, query auto completion (QAC), also known as type-ahead and auto-complete suggestion, refers to the following functionality: given a prefix consisting of a number of characters entered into a search box, the user interface proposes alternative ways of extending the prefix to a full query. QAC helps users to formulate their query when they have an intent in mind but not a clear way of expressing this in a query. It helps to avoid possible spelling mistakes, especially on devices with small screens. It saves keystrokes and cuts down the search duration of users which implies a lower load on the search engine, and results in savings in machine resources and maintenance. Because of the clear benefits of QAC, a considerable number of algorithmic approaches to QAC have been proposed in the past few years. Query logs have proven to be a key asset underlying most of the recent research. This monograph surveys this research. It focuses on summarizing the literature on QAC and provides a general understanding of the wealth of QAC approaches that are currently available. A Survey of Query Auto Completion in Information Retrieval is an ideal reference on the topic. Its contributions can be summarized as follows: It provides researchers who are working on query auto completion or related problems in the field of information retrieval with a good overview and analysis of state-of-the-art QAC approaches. In particular, for researchers new to the field, the survey can serve as an introduction to the state-of-the-art. It also offers a comprehensive perspective on QAC approaches by presenting a taxonomy of existing solutions. In addition, it presents solutions for QAC under different conditions such as available high-resolution query logs, in-depth user interactions with QAC using eye-tracking, and elaborate user engagements in a QAC process. It also discusses practical issues related to QAC. Lastly, it presents a detailed discussion of core challenges and promising open directions in QAC.


Importantly query autocompletion is a challenging task. by F Cai Cited by 108 In information retrieval query auto completion QAC also known as type ahead Xiao et al. Three major challenges are observed for a. Information Retrieval. at the best online .


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Its contributions can be summarized as follows It provides researchers who are working on query auto completion or related problems in the field of information retrieval with a. 2014b and autocomplete suggestion Jain and Mishne 2010 refers to the foll. F Cai W Chen. Maarten de Rijke2 f.caiuva.nl derijkeuva.nl. Question answering is a difficult form of information retrieval characterised by information needs that are at least somewhat expressed as natural language statements or questions and was used as one of the most natural type of human computer communication. A survey of query auto completion in information retrieval. Our proposal generally can promote users intended query returning the correct query candidate early in the QAC list. Read A Survey of Query Auto Completion in Information Retrieval Foundations and Trends R in Information Retrieval book reviews author details and more at Amazon.in. Fast and free shipping free returns cash on . Assessment Biopsychology Comparative Cognitive Developmental Language Individual differences Personality Philosophy Social Methods Statistics Clinical Educational Industrial Professional items World psychology. It is also termed as dynamic. Altmetric Badge . In information retrieval query auto completion QAC also known as typeahead Xiao et al. A Survey of Query Auto Completion in Information Retrieval Cai Fei de Rijke ILLC Maarten Amazon.com.mx Libros. Buy A Survey of Query Auto Completion in Information Retrieval at Walmart.com.


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