In plain, uncomplicated language, and using detailed examples to explain the key concepts, models, and algorithms in vertical search ranking, Relevance Ranking for Vertical Search Engines teaches readers how to manipulate ranking algorithms to achieve better results in real-world applications. This reference book for professionals covers concepts and theories from the fundamental to the advanced, such as relevance, query intention, location-based relevance ranking, and cross-property ranking. It covers the most recent developments in vertical search ranking applications, such as freshness-based relevance theory for new search applications, location-based relevance theory for local search applications, and cross-property ranking theory for applications involving multiple verticals. Foreword by Ron Brachman, Chief Scientist and Head, Yahoo! Labs Introduces ranking algorithms and teaches readers how to manipulate ranking algorithms for the best results Covers concepts and theories from the fundamental to the advanced Discusses the state of the art: development of theories and practices in vertical search ranking applications Includes detailed examples, case studies and real-world situations

A culmination of the authors' years of extensive research on the topic, this book provides a complete, self-contained introduction to advances in relational data clustering. It presents a novel theoretical framework as well as the most state-of-the-art models for relational data clustering. The authors also describe a family of new algorithms for various relational clustering problems arising in a wide range of important applications. Concepts and theory are explained through detailed application examples and the book includes a comprehensive reference list of related literature.