Part of the “SpringerBriefs in Electrical and Computer Engineering” series, this comprehensive resource delves into the specialized field of group recommender systems, which are designed to generate recommendations for groups of users rather than individuals.

The book’s content is broad and thorough, covering various aspects of this niche technology. Key highlights include:

• An in-depth exploration of the latest algorithms used in group recommender systems, crucial for understanding the current technological landscape and the underlying mechanics of these systems;
• Practical applications of group recommender systems in industry, giving readers insights into how these systems are employed in real-world scenarios;
• A focus on decision biases within groups, including methods for de-biasing, which is essential for creating fair and effective recommendation systems;
• The human element in group decision-making, which is important for understanding the context in which these systems are used;
• Related applications, broadening the scope of the book and providing a more comprehensive understanding of the field; and
• Open research issues, which is a valuable resource for researchers looking to explore new avenues in group recommendation systems.

The book seems to position itself as a valuable resource for a broad audience interested in the intersection of technology, group dynamics, and recommendation systems, despite its technical content. This includes experienced researchers in the field, practitioners looking to apply these systems in real-world settings, and students seeking to learn about this specialized area of technology. It provides a comprehensive look at topics relevant to group recommender systems, not only the technical foundations and algorithms but also the psychological and social dynamics at play when groups make decisions. This holistic approach is beneficial for readers who seek to understand both the “how” and the “why” behind these systems.

The authors’ interdisciplinary approach blends elements from computer science (CS), psychology, and the social sciences. This can provide readers with a more nuanced understanding of the complexities involved in designing and implementing group recommender systems. The inclusion of industrial applications and case studies helps bridge the gap between theory and practice, which will be particularly beneficial for practitioners and researchers who are interested in seeing how the principles and theories discussed in the book translate into actual systems.

Furthermore, by discussing open research issues, the book not only serves as a current reference but also as a launchpad for future studies and developments in the field. This forward-looking perspective is crucial in a rapidly evolving area like recommender systems. The discussion around decision biases and de-biasing approaches indicates a focus on the practical and ethical implications of group recommender systems. This is particularly relevant in today’s context, where there is an increasing emphasis on fairness, transparency, and accountability in artificial intelligence (AI) systems.

While the book presents a comprehensive overview and in-depth analysis of group recommender systems, there are areas where it may have limitations or potential for improvement:

• For readers who are new to the field or lack a strong background in CS or data analytics, the in-depth discussion of algorithms and technical aspects might be challenging to understand. The book might benefit from including more foundational explanations or a primer section for nonexperts.
• The specific focus on group recommender systems, while thorough, may not appeal to a broader audience interested in recommender systems in general. Readers looking for a more general overview of recommender systems might find the content too specialized.
• Given the fast pace of technological advancement in AI and recommender systems, some of the content, especially regarding state-of-the-art algorithms, might become outdated quickly. Continuous updates or a digital companion to the book could address this issue.
• While the book covers industrial applications and case studies, the balance between theoretical concepts and practical, real-world applications might not suffice for practitioners seeking more hands-on guidance and implementation strategies.
• If the book predominantly reflects the viewpoints and research of its authors, it may lack a diversity of perspectives that is critical in a field as interdisciplinary as group recommender systems. Including contributions or case studies from a wider range of professionals in the field could enhance its breadth.
• While the book addresses decision biases and de-biasing approaches, it could further explore the ethical implications of group recommender systems, especially in contexts with significant social impact. A deeper examination of how these systems can influence group dynamics and decision-making processes in various cultural and social contexts would be beneficial.

Group recommender systems: an introduction serves as an essential reference for both researchers and practitioners in the field. It combines technical details with practical applications and psychological insights, making it a well-rounded resource for anyone interested in the development and implementation of group recommender systems. It offers a rich, multi-dimensional exploration of its subject matter, and promises to be a key resource for anyone interested in the development and application of recommender systems, especially those focused on group settings. However, it may face challenges in terms of accessibility for a broader audience, keeping pace with technological advancements, and providing a more diverse and practical perspective on the subject.