Dr. James Neve Publishes New Book "Reciprocal Recommender Systems" from Springer
Dr. James Neve Publishes New Book "Reciprocal Recommender Systems" from Springer
ELSOUL LABO B.V. (Headquartered in Amsterdam, the Netherlands; CEO: Fumitake Kawasaki) is pleased to announce that its Technical Advisor, Dr. James Neve, has published a new book entitled Reciprocal Recommender Systems with Springer.
Overview of the New Book: Reciprocal Recommender Systems
This book offers a comprehensive introduction to Reciprocal Recommender Systems (RRS), covering both theoretical foundations and practical implementation examples. It is designed for a wide readership—from beginners to seasoned practitioners. Reciprocal Recommender Systems have garnered significant attention as an advanced machine learning technology that optimally matches “people with people,” such as in matchmaking apps, job-matching or career-switching websites, and mentor-mentee matching services.
-
Reciprocity
Unlike traditional one-way recommendations, where “users choose items,” RRS requires both users to choose each other, increasing both the complexity and importance of successful matches. -
Compatibility
When recommending a potential match, the system also considers whether “the other party is likely to choose me,” estimating how well both users’ preferences and conditions align. -
Complex Recommendation Process
Because the ultimate goal is to achieve mutual satisfaction (a successful “match”), algorithm design must go beyond one-sided preference analysis and incorporate the interactions between users.
Structure and Key Features
-
Comprehensive Coverage: From Theory to Implementation
The introductory chapter clearly explains the theoretical background of reciprocal recommender systems, followed by step-by-step examples of the most successful algorithms. Readers with a basic knowledge of machine learning can quickly implement a number of algorithms presented in the book, aided by the included code samples. -
Balanced Approach: Practical Use Cases and Cutting-Edge Research
While the algorithm explanations are accessible to industry professionals, the book also delves into emerging research topics, such as the application of modern matching theory. This combination provides valuable insights not only for developers seeking to optimize systems but also for academic researchers exploring new methodologies. -
Insights for Future System Design
By reading this book, readers gain a comprehensive understanding of the latest developments in reciprocal recommender systems, along with the foundational knowledge and applied skills needed to design and implement their own RRS solutions. From talent matching to matchmaking apps, the book expands possibilities for any service that brings “people to people.”
Book Details
- Title: Reciprocal Recommender Systems (SpringerBriefs in Computer Science)
- Author: James Neve
- Publisher: Springer
- ISBN: 978-3031851025
- Amazon.com: https://www.amazon.com/Reciprocal-Recommender-Systems-James-Neve/dp/3031851021/ref=sr_1_1
About the Author: Dr. James Neve
Dr. James Neve has worked as a Machine Learning Researcher for online dating services. He also serves as Technical Advisor at ELSOUL LABO B.V. in Amsterdam. After earning his Ph.D. in Machine Learning with a focus on reciprocal recommender systems from the University of Bristol (UK), Dr. Neve has published numerous research findings on reciprocal recommendation at major conferences such as ACM RecSys.
In 2025, Dr. Neve founded Aisara, Inc. in Tokyo, which provides expert guidance on implementing optimal AI and machine learning technologies for organizations facing diverse challenges. Drawing on his end-to-end experience from design to deployment, he offers high-resolution, accurate advice and practical solutions.
Aisara, Inc. Official Website: https://aisara.jp/en/
