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An Intelligent Fuzzy Rule-Based Personalized News Recommendation Using Social Media Mining.

Saravanapriya Manoharan1, Radha Senthilkumar1

  • 1Department of Information Technology, Madras Institute of Technology, Anna University, Chennai, India.

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This study introduces a fuzzy logic approach to predict user interests and recommend relevant news articles, including viral social media content. The method achieved high accuracy in predicting user interests and recommending news from various sources.

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Area of Science:

  • Information Science
  • Computer Science

Background:

  • Recommending relevant news is challenging due to evolving user interests and abundant online content.
  • Viral news spreads rapidly via social media, posing a challenge for traditional news recommendation systems.

Purpose of the Study:

  • To develop a fuzzy logic approach for predicting diversified user interests and categories.
  • To analyze and recommend viral news articles from social media feeds (Facebook, Twitter) based on predicted user interests.

Main Methods:

  • Analyzing implicit user profiles to predict diversified interests using a fuzzy logic approach.
  • Mining social media feeds (Facebook, Twitter) to identify and categorize viral news articles.
  • Retrieving fresh news articles and integrating viral news for personalized recommendations.

Main Results:

  • The proposed approach achieved 84.238% accuracy in predicting overall user interests across categories.
  • Recommendation accuracy reached 100% from news feeds, 90% from Facebook, and 100% from Twitter with respect to user interests.

Conclusions:

  • The fuzzy logic approach effectively predicts user interests and enhances news recommendation systems.
  • Integrating social media analysis improves the relevance and timeliness of recommended news articles.