AI-Powered Media Personalization

AI-Powered Media Personalization uses large language models and advanced recommendation algorithms to tailor news, articles, and media feeds to each user’s interests, reading history, and intent. By dynamically profiling audiences and optimizing content, tags, and search results in real time, it boosts engagement, increases session length, and drives higher subscription and ad revenues for media companies.

The Problem

“Real-time personalized feeds and search that lift engagement and subscriptions”

Organizations face these key challenges:

Homepage/feed engagement stagnates despite more content production

Cold-start users and new articles perform poorly due to lack of signals

Over-personalization creates filter bubbles and hurts content diversity

Search and feed rankings disagree, causing inconsistent user experience

AI-Powered Media Personalization

The Problem

Impact When Solved

The Shift

Technologies

Key Players

Real-World Use Cases

Argoid AI-Powered Recommendation Engine

Shaped | Recommendations and Search

Schibsted Personalised News & Content Recommendations

Language Models and Topic Models for Personalizing Tag Recommendation

Personalized Text-Based Recommendation System