Recommender-systems.org

9:00 Xavier Amatriain - Netflix machine learning Background: Research/Engineering Director at Netflix Why didn’t they use Netflix final prize? The model complexity created engineering challenges that didn’t justify the business benefits...

Recommendation engines, otherwise known as recommender systems, suggest content based on previous behavior or purchases. Such systems typically use one of two approaches: Collaborative filtering creates a predictive model based on a user’s previous...

This study explores new ways of tag-based personalized recommendation by relieving the assumption that tags assigned by a user occur independently of each other. The new methods profile users using tag co-occurrence networks, upon which link-based node...

Editor’s Note: This presentation was given by Maurits van der Goes at GraphConnect Europe in April 2016. Here’s a quick review of what he covered: Why do we need virtual teams? How to develop a graph recommendation engine How to overcome filtering...

Bayesian probabilistic modeling provides a powerful framework for building flexible models to incorporate latent structures through likelihood model and prior. When we specify a model, we make certain assumptions about the underlying data-generating...