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 situationsEach patient visit to an outpatient care facility or hospital stay in an inpatient ward generates a great volume of data, ranging from ... It is widely believed that effective and comprehensive use of patient care data created in day-to-day clinical settings has the ... As a result, onerous, costly, and error-prone manual chart reviews are often needed in order to reuse the data in direct patient care ... http://dx.doi.org/10.1016/B978-0-12-407171-1.00003-4 Ac 2014 Elsevier Inc. All rights reserved.
|Title||:||Relevance Ranking for Vertical Search Engines|
|Author||:||Bo Long, Yi Chang|
|Publisher||:||Newnes - 2014-01-25|