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Neural networks, which provide the basis for deep learning, are a class of machine learning methods that are being applied to a diverse array of fields in business, health, technology, and research. In this chapter, we survey some of the key features of deep neural networks and aspects of their design and architecture. We give an overview of some of the different kinds of networks and their applications and highlight how these architectures are used for business applications such as recommender systems. We also provide a summary of some of the considerations needed for using neural network models and future directions in the field.

Collaboration is often required for activities that are too complex or difficult to be dealt with by a single individual. Many situations requiring information-seeking activities also call for people to work together. Often the methods, systems, and tools that provide access to information assume that they are used only by individuals working on their tasks alone. This review points to the need to acknowledge the importance of collaboration in information-seeking processes, to study models, and to develop systems that are specifically designed to enable collaborative information seeking (CIS) tasks. This chapter reviews the literature from various domains including library and information science, human–computer interaction, collaborative systems, and information retrieval. Focus of the review is on the extent to which people work together on information seeking tasks and the systems and tools that are available for them to be successful. Since CIS occurs in the broader context of collaboration in general, a review of literature about collaborations is first undertaken to define it and place it into context with related terms such as cooperation and communication. A more focused review of research follows relating CIS to systems that have attempted to support such interactions. Included are identification and synthesis of a number of core issues in the field and how best to evaluate systems and collaborative tools. Key lessons learned from the review are summarized, and gaps in the literature identified to spur future research and study.

The Internet, the Web and social media have radically transformed a number of core pillars of our social fabric. The way billions of citizens work, interact and socialise is underpinned by our global network infrastructure. Unfortunately, we have also seen a number of negative effects from this transformation. As has been widely publicised, undesirable impacts include the spread of disinformation and fake news; attacks on democratic elections and the ‘weaponisation’ of personal data. This article describes some of the technological approaches that are being taken to address some of the above issues. At the core of these technologies are notions around decentralisation. With blockchains it is possible that citizens can create their own ‘self-sovereign’ identity – the digital equivalent of writing one's name onto a piece of paper – and acquiring verification through blockchain-based techniques. An approach to alleviating the ‘weaponisation’ of personal and sensitive data is to give citizens their own data store. Initiatives such as Sir Tim Berners-Lee's Solid allow users to store, manage and control their own data according to any personal preferences or constraints. We believe that a combination of personal data stores and blockchains will lead to a new type of resilient communication and collaboration mechanism, whereby personal rights and empowerment are enhanced and transparency at the community level is integral.

Purpose — This chapter illustrates and explains the ambiguity and vagueness of the term social search and aims at describing and classifying the heterogeneous landscape of social search implementations on the WWW.

Methodology/approach — We have looked at different definitions as well as the context of social search by carrying out an extensive literature review, and tried to unify and enhance existing ideas and concepts. Our definition of social search is illustrated by a general review of existing social search engines, which are analyzed and described by their specific features and social aspects.

Findings — The chapter presents a discussion of social search as well as a comparison of existing social search engines.

Social implications — The definition of social search and the comparison of social search engines summarize the many ways people can search the web together and allow for an assessment of future developments in this area.

Originality/value of paper — Although different attempts to define social search have been made in the past, we present an argumentation that unifies some existing definitions and which is different from other interpretations of the social search concept. We present an overview and a comparison of the different genres of social search engines.

Social and technological changes of the 21st century influence how and what students learn while in college. New research about student learning suggests a critical need for higher education to reform teaching and learning methods. Experiential and inquiry-based learning (IBL) are essential to engaging students and achieving the type of learning demanded by today’s global workforce. These skills include critical analysis, systems thinking, problem-solving, and spanning cultural and disciplinary boundaries. For decades, international educators purported that education abroad provided these skills for participants, yet recent research suggests that the same factors inhibiting deep learning on campus can also affect global, experiential environments. No longer can faculty members assume that students will learn from experience alone; they must intentionally construct activities accounting for the specific characteristics and needs of learners. This chapter outlines trends influencing student learning, making the case that traditional, content-based, directed instruction is poorly suited for student learning in the 21st century. The authors suggest that applying experiential and inquiry-based practices is essential to constructing effective education abroad program. Case studies, strategies, tools, and resources are provided to assist faculty with developing competencies to teach through an experiential and inquiry-based pedagogical framework.

The aim of this chapter is to present in a pedagogical way the main digital tools used by tourism-related businesses, especially by hospitality businesses. The main purpose of this chapter is to illustrate our discussion with concrete examples and to give a set of advices for efficient use of those tools.

Literature review was conducted on conceptual issues, as well as managerial and marketing aspects of digital tools, their value and use in the hospitality industry.

This chapter highlights the fact that needs in terms of digitalisation depend on the size of the hotel. The main differences can be explained by differences in terms of hotel capacity, and digital technologies should be customised to different types of structures.

This chapter is exploratory in nature, based on a literature review.

It provides clear and practical guidance about the way independent hospitality businesses could use digital tools for marketing purposes. It also suggests the most efficient digital technologies to improve their performance in the field of marketing and customer relationship management.

The chapter demonstrates the huge gap between best practices in the hospitality industry and the way independent enterprises really use, in practice, the digital tools for marketing purposes. It shows how digital technologies could be used in a more efficient way, to take advantage of their full potential.

Purpose – As researchers need partners to collaborate with, this study aims to provide author recommendation for academic researchers for potential collaboration, conference planning, and compilation of scientific working groups with the help of social information. Hereby the chapter analyzes and compares different similarity metrics in information and computer science.

Methodology/approach – The study uses data from the multidiscipline information services Web of Science and Scopus as well as the social bookmarking service CiteULike to measure author similarity and recommend researchers to unique target researchers. The similarity approach is based on author co-citation, bibliographic coupling of authors and collaborative filtering methods. The developed clusters and graphs are then evaluated by these target researchers.

Findings – The analysis shows, for example, that different methods for social recommendation complement each other and that the researchers evaluated user- and tag-based data from a social bookmarking system positively.

Research limitations/implications – The present study, providing author recommendation for six target physicists, is supposed to be a starting point for further approaches on social academic author recommendation.

Practical implications – The chapter investigates in recommendation methods and similarity algorithm models as basis for an implementation of a social recommendation system for researchers in academics and knowledge-intensive organizations.

Originality/value of chapter – The comparison of different similarity measurements and the user evaluation provide new insights into the construction of social data mining and the investigation of personalized recommendation.