A Practical Guide to Building Recommender Systems
The document “A Practical Guide to Building Recommender Systems” delves into the intricacies of constructing effective recommendation systems. It explores the essential components and methodologies involved in developing these systems, emphasizing practical implementation strategies. Drawing insights from various sources, the guide likely covers topics such as data augmentation, AI applications, database management, and the utilization of AI tools like Langchain. By leveraging a combination of theoretical knowledge and real-world examples, the document aims to provide readers with a comprehensive understanding of how to design and implement recommender systems successfully.
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But how exactly does the recommender algorithm work?
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?...
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Mendeley Suggest Architecture
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 h...
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Impression Discounting
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...
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Testing Recommenders
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 co...
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Don’t Look Stupid
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 p...
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Evaluation Metrics – Part 1
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 ...
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Overview of Recommender Algorithms – Part 5
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...
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Recommender Systems in Netflix
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 u...
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Mendeley Suggest
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...
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Evaluations
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...
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Dithering
In this post we’ll look at a relatively low cost but high value technique for improving the quality of your recommendations, named dithering. It’s a technique that re-orders a list of recommendations ...
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The Components of a Recommender System
A recommender system is made up of five core components (Figure 1). This post is intended to give the big picture. In future posts we’ll jump into details. Arguably, the core component is the one that...
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