The seven companies listed here cover the realistic range of what a buyer will encounter in 2026: embedded ML teams that own ...

Large-scale recommendation systems are becoming harder to improve because they no longer operate as isolated models. Modern ...

Abstract: Due to aggressive urbanization (with population size), waste increases exponentially, resulting in environmental damage. Even though it looks challenging, such an issue can be controlled if ...

LinkedIn is a leader in AI recommender systems, having developed them over the last 15-plus years. But getting to a next-gen recommendation stack for the job-seekers of tomorrow required a whole new ...

Google published a research paper about helping recommender systems understand what users mean when they interact with them. Their goal with this new approach is to overcome the limitations inherent ...

This month marks a decade since Netflix – the world’s most influential and widely subscribed streaming service – launched in Australia. Since then the media landscape has undergone significant ...

This valuable study presents a machine learning model to recommend effective antimicrobial drugs from patients' samples analyzed with mass spectrometry. The evidence supporting the claims of the ...

Ashkan's research concerns applied data science, with a special focus on medical informatics and healthcare analytics. His research covers: 1) design and development of scalable and intelligent ...

This article introduces a recommendation system that merges a knowledge-based (attribute-based) approach with collaborative filtering, specifically addressing the challenges of the pure-cold start ...

If you’ve ever used the Internet, you’ve encountered a recommender system. Recommender systems are complex sets of algorithms used by companies to make predictions about what you might want to buy, ...