Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling, Chevalier Max, Julien Christine, Soule-Dupuy Chantal
Автор: Christine Urquhart Название: Information Systems: Process and practice ISBN: 1783302410 ISBN-13(EAN): 9781783302413 Издательство: Facet Рейтинг: Цена: 15312.00 р. Наличие на складе: Нет в наличии.
Описание: This book adopts a holistic interpretation of information architecture, to offer a variety of methods, tools, and techniques that may be used when designing websites and information systems that support workflows and what people require when `managing information`.
Автор: Fiori Название: Innovative Document Summarization Techniques ISBN: 1466650192 ISBN-13(EAN): 9781466650190 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 25502.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The prevalence of digital documentation presents some pressing concerns for efficient information retrieval in the modern age. Readers want to be able to access the information they desire without having to search through a mountain of unrelated data, so algorithms and methods for effectively seeking out pertinent information are of critical importance.Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding evaluates some of the existing approaches to information retrieval and summarisation of digital documents, as well as current research and future developments. This book serves as a sounding board for students, educators, researchers and practitioners of information technology, advancing the ongoing discussion of communication in the digital age.
Автор: Ai-Suqri, Lillard & Ai-Saleem Название: Information Access And Library User Needs In Developing Countries ISBN: 1466643536 ISBN-13(EAN): 9781466643536 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 25502.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: While high quality library and information services continue to thrive and strengthen economic and social development, much of the knowledge that exists on user’s needs and behaviours is fundamentally based on the results of users in English-speaking, western developed countries. <br><br><em>Information Access and Library User Needs in Developing Countries</em> highlights the struggles that developing countries face in terms of information gaps and information-seeking user behaviour. The publication highlights ways in which users in developing countries can benefit from properly implementing LIS services. Researchers, academics, and practitioners interested in the design and delivery of information services will benefit from this collection of research.
Автор: Alessandro Fiori Название: Trends and Applications of Text Summarization Techniques ISBN: 1522593748 ISBN-13(EAN): 9781522593744 Издательство: Mare Nostrum (Eurospan) Цена: 23423.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: While the availability of electronic documents increases exponentially with advancing technology, the time spent to process this wealth of resourceful information decreases. Content analysis and information extraction must be aided by summarization methods to quickly parcel pieces of interest and allow for succinct user familiarization in a simple, efficient manner. Trends and Applications of Text Summarization Techniques is a pivotal reference source that explores the latest approaches of document summarization including update, multi-lingual, and domain-oriented summarization tasks and examines their current real-world applications in multiple fields. Featuring coverage on a wide range of topics such as parallel construction, social network integration, and evaluation metrics, this book is ideally designed for information technology practitioners, computer scientists, bioinformatics analysts, business managers, healthcare professionals, academicians, researchers, and students.
Автор: Alessandro Fiori Название: Trends and Applications of Text Summarization Techniques ISBN: 152259373X ISBN-13(EAN): 9781522593737 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 28274.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: While the availability of electronic documents increases exponentially with advancing technology, the time spent to process this wealth of resourceful information decreases. Content analysis and information extraction must be aided by summarization methods to quickly parcel pieces of interest and allow for succinct user familiarization in a simple, efficient manner.
Trends and Applications of Text Summarization Techniques is a pivotal reference source that explores the latest approaches of document summarization including update, multi-lingual, and domain-oriented summarization tasks and examines their current real-world applications in multiple fields. Featuring coverage on a wide range of topics such as parallel construction, social network integration, and evaluation metrics, this book is ideally designed for information technology practitioners, computer scientists, bioinformatics analysts, business managers, healthcare professionals, academicians, researchers, and students.
Автор: Graham Shawn Et Al Название: Exploring Big Historical Data: The Historian`S Macroscope ISBN: 1783266376 ISBN-13(EAN): 9781783266371 Издательство: World Scientific Publishing Рейтинг: Цена: 5069.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Digital Humanities have arrived at a moment when digital Big Data is becoming more readily available, opening exciting new avenues of inquiry but also new challenges. This pioneering book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines, or designing and using their own tools to process large amounts of information.Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future. It represents the current state-of-the-art thinking in the field and exemplifies the way that digital work can enhance public engagement in the humanities.Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.The companion website to Exploring Big Historical Data can be found at www.themacroscope.org/. On this site you will find code, a discussion forum, essays, and datafiles that accompany this book.
Автор: Graham Shawn Et Al Название: Exploring Big Historical Data: The Historian`S Macroscope ISBN: 1783266082 ISBN-13(EAN): 9781783266081 Издательство: World Scientific Publishing Рейтинг: Цена: 12830.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Digital Humanities have arrived at a moment when digital Big Data is becoming more readily available, opening exciting new avenues of inquiry but also new challenges. This pioneering book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines, or designing and using their own tools to process large amounts of information.Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future. It represents the current state-of-the-art thinking in the field and exemplifies the way that digital work can enhance public engagement in the humanities.Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.The companion website to Exploring Big Historical Data can be found at www.themacroscope.org/. On this site you will find code, a discussion forum, essays, and datafiles that accompany this book.
Описание: Nowadays, fashion has become an essential aspect of people's daily life. As each outfit usually comprises several complementary items, such as a top, bottom, shoes, and accessories, a proper outfit largely relies on the harmonious matching of these items. Nevertheless, not everyone is good at outfit composition, especially those who have a poor fashion aesthetic. Fortunately, in recent years the number of online fashion-oriented communities, like IQON and Chictopia, as well as e-commerce sites, like Amazon and eBay, has grown. The tremendous amount of real-world data regarding people's various fashion behaviors has opened a door to automatic clothing matching. Despite its significant value, compatibility modeling for clothing matching that assesses the compatibility score for a given set of (equal or more than two) fashion items, e.g., a blouse and a skirt, yields tough challenges: (a) the absence of comprehensive benchmark; (b) comprehensive compatibility modeling with the multi-modal feature variables is largely untapped; (c) how to utilize the domain knowledge to guide the machine learning; (d) how to enhance the interpretability of the compatibility modeling; and (e) how to model the user factor in the personalized compatibility modeling. These challenges have been largely unexplored to date. In this book, we shed light on several state-of-the-art theories on compatibility modeling. In particular, to facilitate the research, we first build three large-scale benchmark datasets from different online fashion websites, including IQON and Amazon. We then introduce a general data-driven compatibility modeling scheme based on advanced neural networks. To make use of the abundant fashion domain knowledge, i.e., clothing matching rules, we next present a novel knowledge guided compatibility modeling framework. Thereafter, to enhance the model interpretability, we put forward a prototype wise interpretable compatibility modeling approach. Following that, noticing the subjective aesthetics of users, we extend the general compatibility modeling to the personalized version. Moreover, we further study the real-world problem of personalized capsule wardrobe creation, aiming to generate a minimum collection of garments that is both compatible and suitable for the user. Finally, we conclude the book and present future research directions, such as the generative compatibility modeling, virtual try-on with arbitrary poses, and clothing generation.
Описание: Nowadays, fashion has become an essential aspect of people's daily life. As each outfit usually comprises several complementary items, such as a top, bottom, shoes, and accessories, a proper outfit largely relies on the harmonious matching of these items. Nevertheless, not everyone is good at outfit composition, especially those who have a poor fashion aesthetic. Fortunately, in recent years the number of online fashion-oriented communities, like IQON and Chictopia, as well as e-commerce sites, like Amazon and eBay, has grown. The tremendous amount of real-world data regarding people's various fashion behaviors has opened a door to automatic clothing matching. Despite its significant value, compatibility modeling for clothing matching that assesses the compatibility score for a given set of (equal or more than two) fashion items, e.g., a blouse and a skirt, yields tough challenges: (a) the absence of comprehensive benchmark; (b) comprehensive compatibility modeling with the multi-modal feature variables is largely untapped; (c) how to utilize the domain knowledge to guide the machine learning; (d) how to enhance the interpretability of the compatibility modeling; and (e) how to model the user factor in the personalized compatibility modeling. These challenges have been largely unexplored to date. In this book, we shed light on several state-of-the-art theories on compatibility modeling. In particular, to facilitate the research, we first build three large-scale benchmark datasets from different online fashion websites, including IQON and Amazon. We then introduce a general data-driven compatibility modeling scheme based on advanced neural networks. To make use of the abundant fashion domain knowledge, i.e., clothing matching rules, we next present a novel knowledge guided compatibility modeling framework. Thereafter, to enhance the model interpretability, we put forward a prototype wise interpretable compatibility modeling approach. Following that, noticing the subjective aesthetics of users, we extend the general compatibility modeling to the personalized version. Moreover, we further study the real-world problem of personalized capsule wardrobe creation, aiming to generate a minimum collection of garments that is both compatible and suitable for the user. Finally, we conclude the book and present future research directions, such as the generative compatibility modeling, virtual try-on with arbitrary poses, and clothing generation.
Описание: Chapter 1. Graded Relevance.- Chapter 2. Experiments on Cross-Language Information Retrieval using Comparable Corpora of Chinese, Japanese, and Korean Languages.- Chapter 3. Text Summarization Challenge.- Chapter 4. Challenges in Patent Information Retrieval.- Chapter 5. Multi-Modal Summarization.- Chapter 6. Opinion Analysis Corpora Across Languages.- Chapter 7. Patent Translation.- Chapter 8. Component-Based Evaluation for Question Answering.- Chapter 9. Temporal Information Access.- Chapter 10. SogouQ.- Chapter 11. Evaluation of Information Access with Smartphones.- Chapter 12. Mathematical Information Retrieval.- Chapter 13. Experiments in Lifelog Organisation and Retrieval at NTCIR.- Chapter 14. The Future of Information Retrieval Evaluation.
Описание: Chapter 1. Graded Relevance.- Chapter 2. Experiments on Cross-Language Information Retrieval using Comparable Corpora of Chinese, Japanese, and Korean Languages.- Chapter 3. Text Summarization Challenge.- Chapter 4. Challenges in Patent Information Retrieval.- Chapter 5. Multi-Modal Summarization.- Chapter 6. Opinion Analysis Corpora Across Languages.- Chapter 7. Patent Translation.- Chapter 8. Component-Based Evaluation for Question Answering.- Chapter 9. Temporal Information Access.- Chapter 10. SogouQ.- Chapter 11. Evaluation of Information Access with Smartphones.- Chapter 12. Mathematical Information Retrieval.- Chapter 13. Experiments in Lifelog Organisation and Retrieval at NTCIR.- Chapter 14. The Future of Information Retrieval Evaluation.
Название: Collaborative information seeking ISBN: 3319185411 ISBN-13(EAN): 9783319185415 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Collaborative Information Seeking
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