News Recommendation System
AI-Driven Personalized News Delivery
Engaging readers with tailored content using deep learning and behavioral analysis.
Project Overview
News platforms often struggle to hold readers’ attention. The client needed a smart system to keep users engaged by offering content aligned with their preferences and past behavior.
Language:
Python
Models:
Deep Learning, NLP
Storage:
Apache Cassandra
Address:
United States
Industry:
News & Media
The Challenge
Despite quality content, news websites were failing to:
Retain reader interest
Drive repeat traffic
Surface relevant content efficiently
Our Solution
We built a probabilistic recommendation system using deep learning and NLP. The model analyzes user behavior—likes, dislikes, clicks, and reading history—to serve personalized news suggestions.
Key Highlights:
User data modeling through digital footprints
Real-time recommendation engine
Scalable storage with Apache Cassandra
How It Works
Collect reader interaction data (views, likes, time spent)
Train probabilistic models to identify content preferences
Generate and display personalized news feeds dynamically
Impact & Results
Increased content relevance for each reader
Reduced time spent searching for preferred news
Enhanced user engagement and satisfaction
Client Outcome
5–8% increase in web traffic
Better reader retention and interaction
Structured insights into reader preferences for future optimization
Project Snapshot
Language: Python
Models: Deep Learning, NLP
Storage: Apache Cassandra
Location: United States
Industry: News & Media