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{ "item_title" : "Big Data Recommender Systems", "item_author" : [" Osman Khalid", "Samee U. Khan", "Albert Y. Zomaya "], "item_description" : "First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users' data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges. Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/1/78/561/977/1785619772_b.jpg", "price_data" : { "retail_price" : "175.00", "online_price" : "175.00", "our_price" : "175.00", "club_price" : "175.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Big Data Recommender Systems|Osman Khalid

Big Data Recommender Systems : Application Paradigms

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Overview

First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users' data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges.

Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures.

This item is Non-Returnable

Details

  • ISBN-13: 9781785619779
  • ISBN-10: 1785619772
  • Publisher: Institution of Engineering & Technology
  • Publish Date: August 2019
  • Dimensions: 9.4 x 6.3 x 1.2 inches
  • Shipping Weight: 1.9 pounds
  • Page Count: 520

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