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020			_a9783031023262
024	7		_a10.1007/978-3-031-02326-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.I58 _b2021 EB
100	1		_aShah, Chirag P _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _993348
245	1	0	_aTask Intelligence for Search and Recommendation _cby Chirag Shah, Ryen W. White
250			_a1st edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XIX, 140 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Information Concepts Retrieval and Services _x1947-9468
505	0		_aPreface -- Acknowledgments -- Introduction -- Task Frameworks, Expressions, and Representations -- Using Task Construct in IR -- Explicating Task -- Applying Task Information for Search and Recommendations -- Task-Based Evaluation -- Conclusions and Future Directions -- Bibliography -- Authors' Biographies .
520			_aWhile great strides have been made in the field of search and recommendation, there are still challenges and opportunities to address information access issues that involve solving tasks and accomplishing goals for a wide variety of users. Specifically, we lack intelligent systems that can detect not only the request an individual is making (what), but also understand and utilize the intention (why) and strategies (how) while providing information and enabling task completion. Many scholars in the fields of information retrieval, recommender systems, productivity (especially in task management and time management), and artificial intelligence have recognized the importance of extracting and understanding people's tasks and the intentions behind performing those tasks in order to serve them better. However, we are still struggling to support them in task completion, e.g., in search and assistance, and it has been challenging to move beyond single-query or single-turn interactions. The proliferation of intelligent agents has unlocked new modalities for interacting with information, but these agents will need to be able to work understanding current and future contexts and assist users at task level. This book will focus on task intelligence in the context of search and recommendation. Chapter 1 introduces readers to the issues of detecting, understanding, and using task and task-related information in an information episode (with or without active searching). This is followed by presenting several prominent ideas and frameworks about how tasks are conceptualized and represented in Chapter 2. In Chapter 3, the narrative moves to showing how task type relates to user behaviors and search intentions. A task can be explicitly expressed in some cases, such as in a to-do application, but often it is unexpressed. Chapter 4 covers these two scenarios with several related works and case studies. Chapter 5 shows how task knowledge and task models can contribute to addressing emerging retrieval and recommendation problems. Chapter 6 covers evaluation methodologies and metrics for task-based systems, with relevant case studies to demonstrate their uses. Finally, the book concludes in Chapter 7, with ideas for future directions in this important research area.
988			_aSynthesis Collection of Technology_2021
650		7	_2embne _9139854 _aSistemas interactivos (Informática)
650		7	_2embne _9147823 _aRecuperación de la información
700	1		_aWhite, Ryen W. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687913
776	0	8	_iPrinted edition: _z9783031002335
776	0	8	_iPrinted edition: _z9783031011986
776	0	8	_iPrinted edition: _z9783031034541
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02326-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2023 _dz _eIG _zSI