NewsPulse

A News Recommendation System

Our machine learning model predicts reader interests from digital footprints, enhancing engagement and content personalization.

Quick Summary

Utilizing the power of machine learning and data analytics, we developed a predictive model that interpreted 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 analyse 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:

  • Attention span of the readers improved by 10%
  • Content personalization increased, leading to a greater user experience and improved engagement
  • More accurate prediction of reader interests, resulting in highly tailored content delivery

The Tech Stack

The following technologies were key in the development of the predictive model:

  • Python and Keras were used to design and train the machine learning model
  • AWS was used for efficient deployment of the model
  • Cassandra served as the backend database for storing digital footprints.

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