Authors :
C.P. Patidar; Dr. Meena Sharma; Yogesh Katara
Volume/Issue :
Volume 5 - 2020, Issue 10 - October
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/3574cqu
Abstract :
A newspaper divided into various sections like
a city, sports, editorial, international, national,
entertainment etc. All the above sections have equal
importance and different user followers for different
sections. Sometimes there may be a possibility that they
may consist of relevant information but in different
sections and different newspapers. News
Recommendation System can overcome this problem
and suggest relevant news according to user preference
and popularity factor. This research paper investigates
the need for news recommendation using a machine
learning approach to make it more efficient and better.
Hybrid Approach can help to recommend news to users
based on Supervised Machine Learning and Term
Frequency-Inverse Document Frequency (TF-IDF)
Keywords :
Machine Learning, Naïve Bayes, News Recommen- Dation, TF-IDF
A newspaper divided into various sections like
a city, sports, editorial, international, national,
entertainment etc. All the above sections have equal
importance and different user followers for different
sections. Sometimes there may be a possibility that they
may consist of relevant information but in different
sections and different newspapers. News
Recommendation System can overcome this problem
and suggest relevant news according to user preference
and popularity factor. This research paper investigates
the need for news recommendation using a machine
learning approach to make it more efficient and better.
Hybrid Approach can help to recommend news to users
based on Supervised Machine Learning and Term
Frequency-Inverse Document Frequency (TF-IDF)
Keywords :
Machine Learning, Naïve Bayes, News Recommen- Dation, TF-IDF
