NewsPulse
A News Recommendation System
Our machine learning model predicts reader interests from digital footprints, enhancing engagement and content personalization.
Quick Summary
Utilizing machine learning and data analytics, we developed a predictive model that interprets digital footprints to forecast reader interest, resulting in increased reader engagement and attention span.
The Problem
In the media and entertainment industry, retaining reader interest and engagement is a pivotal challenge. The inability to predict and understand reader preferences was leading to decreased attention span and lower user engagement.
The Solution
To address the problem, we developed a predictive model using machine learning algorithms to analyze digital footprints. The model forecasts reader interests, which in turn, allows for personalization of content. The solution was designed with Python and Keras for building and training the machine learning model. AWS was used for model deployment, while Cassandra served as the database for storing the digital footprints.
The Outcomes
The implementation of the predictive model led to significant improvements in user engagement:
The Tech Stack
The following technologies were key in the development of the predictive model:
Ready to Start?
Take the first step towards achieving higher reader engagement today. Our model allows for maximum risk mitigation with our refundable deposit policy. If any critical issues occur, we assure a full refund of your deposit. On advancing to the Proof of Concept stage, the deposit is applied towards the overall project cost