Develop an AI-Powered Personalized News Feed

Traditional news platforms deliver the same content to all users, ignoring individual interests. This leads to user disengagement and information overload. A personalized news feed system curates content based on user behavior, reading history, and preferences, making the news experience relevant and engaging for each individual.

By analyzing users' reading patterns, preferred categories, reading time, and feedback (like/save/share), AI models can predict and recommend articles tailored to each user. Machine learning models like collaborative filtering, content-based filtering, or hybrid recommendation engines ensure high personalization, while NLP helps summarize articles dynamically.

User profiles are built based on their browsing behavior (categories read, time spent, interactions). Machine learning models analyze this data to suggest articles from a news database. Natural Language Processing (NLP) helps extract article keywords and sentiment, matching them with user interests. Continuous feedback (like/dislike/save) fine-tunes recommendations over time.

• Collect user interaction data: article clicks, reading duration, shares, saves.
• Apply collaborative filtering or content-based filtering to recommend articles.
• Use NLP models to extract important keywords and summarize articles.
• Continuously train models based on user feedback to improve personalization accuracy.
• Adapt recommendations in real-time for trending topics and evolving interests.

• Building a Recommendation System with Python
• Text Summarization with Hugging Face Transformers
• News API for Fetching Latest Articles