I’ve worked on lots of recommender systems over the years and one of the most common questions that I have been asked by non-recommendery folk is, “But how exactly does the recommender algorithm work?”. It’s a question that I’ve come to loath, fear and love, in ever changing quantities. I loath it because… Read More But how exactly does the recommender algorithm work?
Kris Jack, Ed Ingold and Maya Hristakeva. Introduction Mendeley Suggest, a personalised research literature recommender, has been live for around nine months so we thought we’d mark this traditional human gestation period with a blog post about its architecture. We’ll present how the architecture currently looks, pointing out which technologies we use, justifying decisions that… Read More Mendeley Suggest Architecture
This is the first in a series of posts on evaluation metrics for recommender systems. It’s important to be able to measure attributes of your recommender so that you can start to understand it better and eventually improve it. These metrics allow you to predict both how well your recommender will perform before you test… Read More Evaluation Metrics – Part 1
Netflix is a company that demonstrates how to successfully commercialise recommender systems. Netflix manages a large collections of movies and television programmes, making the content available to users at any time by streaming them directly to their computer/television. It’s a very profitable company that makes its money through monthly user subscriptions. Users can cancel their… Read More Recommender Systems in Netflix
Mendeley Suggest is an article recommender system for researchers. It’s designed to help them discover new research based on their short and long term interests and to keep them up-to-date with what’s popular and trending in their domains. The first set of recommendations is based on all of the articles that you have added to… Read More Mendeley Suggest
In building a recommender, it’s common to ask the question, how well does it work? Ultimately, you’ll only know when you release it live to users and measure it against your targets such as increasing sales or user activity. It’s unlikely, however, that you’ll get it right first time round and you’ll want to be… Read More Evaluations
Recommender systems have been around for decades, over which time a strong community of academic researchers and industry practitioners has emerged. They invest a copious amount of time and energy into understanding the theory and practice of recommenders. As the recommender community grew, it got increasingly organised and created a series of conferences named The… Read More Discover the research community