Online News Recommendation Systems in Machine Learning
| AUTHOR | Anonymous |
| PUBLISHER | Grin Verlag (02/02/2023) |
| PRODUCT TYPE | Paperback (Paperback) |
Description
Research Paper (postgraduate) from the year 2018 in the subject Computer Science - Applied, grade: A, National University of Modern Languages, Islamabad (Institute of Management Sciences), course: IT, language: English, abstract: Bearing in mind the increasing need for access to personalized news, the current research study aims at developing an online news recommendation system that could offer an optimum online news reading experience in a highly personalized fashion. The study considers major methodologies and perspectives, such as reinforced learning, Q-Learning, Collaborative Filtering and User Profiling, within this domain in order to implement the ONRS system. Online news reading has gained more attention in recent years than ever, particularly based on the increasing dependence of users on smartphones and the internet. Leading a busy lifestyle, end-users find it hard to search for relevant news articles online, and require tools that could provide them with the most needed news feed on the go. Although legacy news recommendation systems do exist, yet they do not offer optimum efficiency and accuracy.
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Product Format
Product Details
ISBN-13:
9783346820839
ISBN-10:
3346820831
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
More Product Details
Page Count:
36
Carton Quantity:
196
Product Dimensions:
5.83 x 0.09 x 8.27 inches
Weight:
0.13 pound(s)
Country of Origin:
US
Subject Information
BISAC Categories
Computers | Languages - General
Computers | Business & Productivity Software - General
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publisher marketing
Research Paper (postgraduate) from the year 2018 in the subject Computer Science - Applied, grade: A, National University of Modern Languages, Islamabad (Institute of Management Sciences), course: IT, language: English, abstract: Bearing in mind the increasing need for access to personalized news, the current research study aims at developing an online news recommendation system that could offer an optimum online news reading experience in a highly personalized fashion. The study considers major methodologies and perspectives, such as reinforced learning, Q-Learning, Collaborative Filtering and User Profiling, within this domain in order to implement the ONRS system. Online news reading has gained more attention in recent years than ever, particularly based on the increasing dependence of users on smartphones and the internet. Leading a busy lifestyle, end-users find it hard to search for relevant news articles online, and require tools that could provide them with the most needed news feed on the go. Although legacy news recommendation systems do exist, yet they do not offer optimum efficiency and accuracy.
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List Price $40.90
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$40.49
