A Survey of Query Auto Completion in Information Retrieval
Seiten
2016
now publishers Inc (Verlag)
978-1-68083-200-6 (ISBN)
now publishers Inc (Verlag)
978-1-68083-200-6 (ISBN)
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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. This book also offers a comprehensive perspective on QAC approaches by presenting a taxonomy of existing solutions.
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.
This is an ideal reference on the topic. Its contributions can be summarized as follows: tt 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.
This is an ideal reference on the topic. Its contributions can be summarized as follows: tt 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.
Contents: 1: Introduction; 2: Query auto completion; 3: Heuristic approaches to query auto completion; 4: Learning-based approaches to query auto completion; 5: Evaluation; 6: Efficiency and robustness; 7: Presentation and interaction; 8: Related tasks; 9: Conclusions; Glossary; Acknowledgements; References.
| Erscheinungsdatum | 13.10.2016 |
|---|---|
| Reihe/Serie | Foundations and Trends® in Information Retrieval |
| Verlagsort | Hanover |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 165 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-68083-200-X / 168083200X |
| ISBN-13 | 978-1-68083-200-6 / 9781680832006 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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