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{ "item_title" : "Relevance Ranking for Vertical Search Engines", "item_author" : [" Bo Long", "Yi Chang "], "item_description" : "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. ", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/0/12/407/171/0124071716_b.jpg", "price_data" : { "retail_price" : "59.95", "online_price" : "59.95", "our_price" : "59.95", "club_price" : "59.95", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Relevance Ranking for Vertical Search Engines|Bo Long

Relevance Ranking for Vertical Search Engines

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Overview

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.

This item is Non-Returnable

Details

  • ISBN-13: 9780124071711
  • ISBN-10: 0124071716
  • Publisher: Morgan Kaufmann Publishers
  • Publish Date: January 2014
  • Dimensions: 9.1 x 7.5 x 0.8 inches
  • Shipping Weight: 1.15 pounds
  • Page Count: 264

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