AI-Driven Editorial Intelligence System for News Front-Page Optimization

Developed an AI-powered editorial intelligence platform that predicts news article engagement and optimizes front-page story placement using machine learning and NLP. The system combines headline analytics, semantic embeddings, engagement scoring, and ranking algorithms to dynamically prioritize stories based on predicted audience traction, relevance, and visibility.

Deep Learning

Interactive Demos

Project Overview

GitHub Repo | Live Demo

Focus Areas

Recommendation Systems · Learning-to-Rank · NLP · Editorial AI · Behavioral Analytics · Explainable ML

Built an AI-driven editorial intelligence platform designed to optimize front-page news ranking using behavioral engagement signals, semantic NLP representations, and learning-to-rank models. Traditional editorial workflows rely heavily on manual curation and heuristic prioritization, so I developed a scalable ranking pipeline capable of predicting article traction and dynamically optimizing story placement for maximum audience engagement.

System Architecture

Developed an end-to-end ranking and editorial optimization pipeline integrating NLP feature engineering, transformer-based semantic embeddings, behavioral interaction analytics, and LightGBM LambdaRank models for large-scale news recommendation and front-page ranking.

Key Contributions

  • Built a large-scale news ranking pipeline processing 11.2M+ impression–article interactions and 100K+ news stories from the Microsoft MIND dataset.

  • Engineered behavioural, contextual, and semantic ranking features including historical CTR, interaction statistics, session diversity, and transformer embeddings.

  • Developed a LightGBM LambdaRank recommendation system for engagement-aware article ranking.

  • Implemented transformer-based semantic representations using Sentence Transformers with optimised deduplicated embedding generation workflows

  • Designed a visibility-aware editorial scoring framework balancing recency, engagement prediction, semantic relevance, and front-page positioning

  • Built and deployed an interactive Streamlit newsroom dashboard with explainable ranking insights, category zoning, editorial controls, and real-time ranking analytics

  • Optimised the ML pipeline for memory-efficient large-scale training using parquet processing, embedding reduction, and sampled ranking workflows


Outcome

The project demonstrates how learning-to-rank systems and NLP-driven editorial intelligence can improve digital news curation through engagement-aware ranking and explainable recommendation workflows. By combining behavioural analytics with semantic article understanding, the platform provides a scalable and data-driven approach to front-page optimisation and newsroom decision support.

Tech Stack

Python · LightGBM · NLP · Sentence Transformers · Streamlit · Pandas · Scikit-learn · Recommendation Systems · Learning-to-Rank · Behavioral Analytics · Explainable AI


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