- RecSysOps: Best Practices for Operating a Large-Scale Recommender System
- Recommender Systems in a Nutshell
- On YouTube’s recommendation system
- Why AI Isn’t Providing Better Product Recommendations
- Two Decades of Recommender Systems at Amazon.com
Overview of how to build the most common types of recommendation systems using Python with basic code snippets.
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…
A deeper look into how YouTube’s recommendation system works.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about recommender systems and the ways they are used.
Leveraging the Surprise package to build a collaborative filtering recommender in Python
Posted by Martin Mladenov, Research Scientist and Chih-wei Hsu, Software Engineer, Google Research Significant advances in machine lear...
by Ehsan Saberian and Justin Basilico
Sorting items by relevance is crucial for information retrieval and recommender systems.
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.…
Unsupervised Learning has been called the closest thing we have to “actual” Artificial Intelligence, in the sense of General AI.
KD stands for Knowledge Discovery. Covering #DataScience #MachineLearning #AI #Analytics. Edited by @mattmayo13. Founded by Gregory Piatetsky-Shapiro.
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.
A Medium publication sharing concepts, ideas, and codes. Share your insights and projects with like-minded readers: http://bit.ly/write-for-tds.
How does Refind curate?
It’s a mix of human and algorithmic curation, following a number of steps:
- We monitor 10k+ sources and 1k+ thought leaders on hundreds of topics—publications, blogs, news sites, newsletters, Substack, Medium, Twitter, etc.
- In addition, our users save links from around the web using our Save buttons and our extensions.
- Our algorithm processes 100k+ new links every day and uses external signals to find the most relevant ones, focusing on timeless pieces.
- Our community of active users gets 5 links every day, tailored to their interests. They provide feedback via implicit and explicit signals: open, read, listen, share, add to reading list, save to «Made me smarter», «More/less like this», etc.
- Our algorithm uses these internal signals to refine the selection.
- In addition, we have expert curators who manually curate niche topics.
The result: lists of the best and most useful articles on hundreds of topics.
How does Refind detect «timeless» pieces?
We focus on pieces with long shelf-lives—not news. We determine «timelessness» via a number of metrics, for example, the consumption pattern of links over time.
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, and many more.
Can I submit a link?
Indirectly, by using Refind and saving links from outside (e.g., via our extensions).
How can I report a problem?
When you’re logged-in, you can flag any link via the «More» (...) menu. You can also report problems via email to email@example.com
Who uses Refind?
100k+ smart people start their day with Refind. To learn something new. To get inspired. To move forward. Our apps have a 4.9/5 rating.
Is Refind free?
Yes, it’s free!
How can I sign up?
Head over to our homepage and sign up by email or with your Twitter or Google account.