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
Introduction When recommending items to users, it’s not a good idea to recommend the same ones over and over again if the user isn’t interacting with them. In a previous post, we discussed a technique called dithering that allows us to change the order of recommended items, creating the illusion of freshness, and reducing the chances… Read More Impression Discounting
Why Test? When I met fellow GroupLens alum Sean McNee, he had a bit of advice for me: Write tests for your code. It took me some time to grasp the wisdom of this — after all, isn’t it just research code? — but testing has been a very valuable tool in our development of… Read More Testing Recommenders
In the last few posts we discussed a number of different algorithms which can be used to generate personalised recommendations for users. Once the recommendations are generated, they often need some post-processing (component 3) before being shown to users. At this point, it’s common for some recommendations to be filtered out and some reranking to… Read More Don’t Look Stupid
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
This is the final part in a five part series on overviewing recommender algorithms. In the first post, we introduced the main types of recommender algorithms by providing a cheatsheet for them. In the second, we covered the different types of collaborative filtering algorithms highlighting some of their nuances and how they differ from one… Read More Overview of Recommender Algorithms – Part 5
This is the fourth in a multi-part post. In the first post, we introduced the main types of recommender algorithms by providing a cheatsheet for them. In the second post, we covered the different types of collaborative filtering algorithms highlighting some of their nuances and how they differ from one another. In the third post,… Read More Overview of Recommender Algorithms – Part 4
This is the third in a multi-part post. In the first post, we introduced the main types of recommender algorithms by providing a cheatsheet for them. In the second post, covered the different types of collaborative filtering algorithms highlighting some of their nuances and how they differ from one another. In this blog post, we’ll… Read More Overview of Recommender Algorithms – Part 3
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
A Practical Guide to Building Recommender Systems 