This volume summarizes the author's work on social information seeking (SIS), and at the same time serves as an introduction to the topic. Sometimes also referred to as social search or social information retrieval, this is a relatively new area of study concerned with the seeking and acquiring of information from social spaces on the Internet. It involves studying situations, motivations, and methods involved in seeking and sharing of information in participatory online social sites, such as Yahoo! Answers, WikiAnswers, and Twitter, as well as building systems for supporting such activities. The first part of the book introduces various foundational concepts, including information seeking, social media, and social networking. As such it provides the necessary basis to then discuss how those aspects could intertwine in different ways to create methods, tools, and opportunities for supporting and leveraging SIS. Next, Part II discusses the social dimension and primarily examines the online question-answering activity. Part III then emphasizes the collaborative aspect of information seeking, and examines what happens when social and collaborative dimensions are considered together. Lastly, Part IV provides a synthesis by consolidating methods, systems, and evaluation techniques related to social and collaborative information seeking. The book is completed by a list of challenges and opportunities for both theoretical and practical SIS work. The book is intended mainly for researchers and graduate students looking for an introduction to this new field, as well as developers and system designers interested in building interactive information retrieval systems or social/community-driven interfaces.

Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.

Compiled by world- class leaders in the field of collaborative information retrieval and search (CIS), this book centres on the notion that information seeking is not always a solitary activity and working in collaboration to perform information-seeking tasks should be studied and supported. Covering aspects of theories, models, and applications the book is divided in three parts: * Best Practices and Studies: providing an overview of current knowledge and state-of-the-art in the field. * New Domains: covers some of the new and exciting opportunities of applying CIS * New Thoughts: focuses on new research directions by scholars from academia and industry from around the world. Collaborative Information Seeking provides a valuable reference for student, teachers, and researchers interested in the area of collaborative work, information seeking/retrieval, and human-computer interaction.

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Today's complex, information-intensive problems often require people to work together. Mostly these tasks go far beyond simply searching together; they include information lookup, sharing, synthesis, and decision-making. In addition, they all have an end-goal that is mutually beneficial to all parties involved. Such "collaborative information seeking" (CIS) projects typically last several sessions and the participants all share an intention to contribute and benefit. Not surprisingly, these processes are highly interactive. Shah focuses on two individually well-understood notions: collaboration and information seeking, with the goal of bringing them together to show how it is a natural tendency for humans to work together on complex tasks. The first part of his book introduces the general notions of collaboration and information seeking, as well as related concepts, terminology, and frameworks; and thus provides the reader with a comprehensive treatment of the concepts underlying CIS. The second part of the book details CIS as a standalone domain. A series of frameworks, theories, and models are introduced to provide a conceptual basis for CIS. The final part describes several systems and applications of CIS, along with their broader implications on other fields such as computer-supported cooperative work (CSCW) and human-computer interaction (HCI). With this first comprehensive overview of an exciting new research field, Shah delivers to graduate students and researchers in academia and industry an encompassing description of the technologies involved, state-of-the-art results, and open challenges as well as research opportunities.

This volume summarizes the author's work on social information seeking (SIS), and at the same time serves as an introduction to the topic. Sometimes also referred to as social search or social information retrieval, this is a relatively new area of study concerned with the seeking and acquiring of information from social spaces on the Internet. It involves studying situations, motivations, and methods involved in seeking and sharing of information in participatory online social sites, such as Yahoo! Answers, WikiAnswers, and Twitter, as well as building systems for supporting such activities. The first part of the book introduces various foundational concepts, including information seeking, social media, and social networking. As such it provides the necessary basis to then discuss how those aspects could intertwine in different ways to create methods, tools, and opportunities for supporting and leveraging SIS. Next, Part II discusses the social dimension and primarily examines the online question-answering activity. Part III then emphasizes the collaborative aspect of information seeking, and examines what happens when social and collaborative dimensions are considered together. Lastly, Part IV provides a synthesis by consolidating methods, systems, and evaluation techniques related to social and collaborative information seeking. The book is completed by a list of challenges and opportunities for both theoretical and practical SIS work. The book is intended mainly for researchers and graduate students looking for an introduction to this new field, as well as developers and system designers interested in building interactive information retrieval systems or social/community-driven interfaces.

Today's complex, information-intensive problems often require people to work together. Mostly these tasks go far beyond simply searching together; they include information lookup, sharing, synthesis, and decision-making. In addition, they all have an end-goal that is mutually beneficial to all parties involved. Such "collaborative information seeking" (CIS) projects typically last several sessions and the participants all share an intention to contribute and benefit. Not surprisingly, these processes are highly interactive. Shah focuses on two individually well-understood notions: collaboration and information seeking, with the goal of bringing them together to show how it is a natural tendency for humans to work together on complex tasks. The first part of his book introduces the general notions of collaboration and information seeking, as well as related concepts, terminology, and frameworks; and thus provides the reader with a comprehensive treatment of the concepts underlying CIS. The second part of the book details CIS as a standalone domain. A series of frameworks, theories, and models are introduced to provide a conceptual basis for CIS. The final part describes several systems and applications of CIS, along with their broader implications on other fields such as computer-supported cooperative work (CSCW) and human-computer interaction (HCI). With this first comprehensive overview of an exciting new research field, Shah delivers to graduate students and researchers in academia and industry an encompassing description of the technologies involved, state-of-the-art results, and open challenges as well as research opportunities.

While 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.