Relevance Ranking for Vertical Search Engines
Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Internet
| Author: | Bo Long, Yi Chang | ISBN: | 9780124072022 |
| Publisher: | Elsevier Science | Publication: | January 25, 2014 |
| Imprint: | Morgan Kaufmann | Language: | English |
| Author: | Bo Long, Yi Chang |
| ISBN: | 9780124072022 |
| Publisher: | Elsevier Science |
| Publication: | January 25, 2014 |
| Imprint: | Morgan Kaufmann |
| Language: | English |
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
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
