10 Best Articles on Recommender Systems
The most useful articles on recommender systems from around the web—beginners to advanced—curated by thought leaders and our community. We focus on timeless pieces and update the list whenever we discover new, must-read articles or videos—make sure to bookmark and revisit this page.
Top 5 Recommender Systems Articles
At a glance: these are the articles that have been most read, shared, and saved on recommender systems by Refind users in 2023 so far.
How to ...?
How to Build a Recommender System
Overview of how to build the most common types of recommendation systems using Python with basic code snippets.
Short Articles
Short on time? Check out these useful short articles on recommender systems—all under 10 minutes.
Why AI Isn’t Providing Better Product Recommendations
If you're interested in obscure things, there are two reasons why your searches for items and products are likely to be less related to your interests than those of your 'mainstream' peers; either…
On YouTube’s recommendation system
A deeper look into how YouTube’s recommendation system works.
Recommender Systems in a Nutshell
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about recommender systems and the ways they are used.
A Simple Approach To Building a Recommendation System
Leveraging the Surprise package to build a collaborative filtering recommender in Python
RecSim: A Configurable Simulation Platform for Recommender Systems
Posted by Martin Mladenov, Research Scientist and Chih-wei Hsu, Software Engineer, Google Research Significant advances in machine lear...
Long Articles
These are some of the most-read long-form articles on recommender systems.
RecSysOps: Best Practices for Operating a Large-Scale Recommender System
by Ehsan Saberian and Justin Basilico
Learning to Rank: A Complete Guide to Ranking using Machine Learning
Sorting items by relevance is crucial for information retrieval and recommender systems.
Two Decades of Recommender Systems at Amazon.com
Brent Smith, Amazon.comGreg Linden, MicrosoftPages: 12–18Abstract—Amazon is well-known for personalization and recommendations, which help customers discover items they might otherwise not have found.…
K-Means Clustering: Unsupervised Learning for Recommender Systems
Unsupervised Learning has been called the closest thing we have to “actual” Artificial Intelligence, in the sense of General AI.
Publications
We monitor hundreds of publications, blogs, newsletters, and news sources in Recommender Systems, including:
KDnuggets
KD stands for Knowledge Discovery. Covering #DataScience #MachineLearning #AI #Analytics. Edited by @mattmayo13. Founded by Gregory Piatetsky-Shapiro.
Google AI
Google AI is focused on bringing the benefits of AI to everyone. In conducting and applying our research, we advance the state-of-the-art in many domains.
Towards Data Science
A Medium publication sharing concepts, ideas, and codes. Share your insights and projects with like-minded readers: http://bit.ly/write-for-tds.
@fastml@sigmoid.social
Notifications about new posts on http://fastml.com. For more on machine learning, see @fastml_extra. Please retweet what you like.
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Every day Refind picks the most relevant links from around the web for you. Picking only a handful of links means focusing on what’s relevant and useful. We favor timeless pieces—links with long shelf-lives, articles that are still relevant one month, one year, or even ten years from now. These lists of the best resources on any topic are the result of years of careful curation.
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It’s a mix of human and algorithmic curation, following a number of steps:
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How many sources does Refind monitor?
We monitor 10k+ content sources on hundreds of topics—publications, blogs, news sites, newsletters, Substack, Medium, Twitter, etc.
Which sources does Refind monitor on recommender systems?
We monitor hundreds of sources on recommender systems, including KDnuggets, Google AI, Towards Data Science, @fastml@sigmoid.social, and many more.
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