- Recommender System Metrics: Comparing Apples, Oranges and Bananas - Feb 11, 2020.
This article will discuss a sometimes-overlooked aspect of what distinguishes recommender systems from other machine learning tasks: added uncertainties of measuring them.
- How YouTube is Recommending Your Next Video - Oct 21, 2019.
If you are interested in learning more about the latest Youtube recommendation algorithm paper, read this post for details on its approach and improvements.
- An Easy Introduction to Machine Learning Recommender Systems - Sep 4, 2019.
Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code.
- Order Matters: Alibaba’s Transformer-based Recommender System - Aug 23, 2019.
Alibaba, the largest e-commerce platform in China, is a powerhouse not only when it comes to e-commerce, but also when it comes to recommender systems research. Their latest paper, Behaviour Sequence Transformer for E-commerce Recommendation in Alibaba, is yet another publication that pushes the state of the art in recommender systems.
- Building a Recommender System, Part 2 - Jul 3, 2019.
This post explores an technique for collaborative filtering which uses latent factor models, a which naturally generalizes to deep learning approaches. Our approach will be implemented using Tensorflow and Keras.
- Building a Recommender System - Apr 4, 2019.
A beginners guide to building a recommendation system, with a step-by-step guide on how to create a content-based filtering system to recommend movies for a user to watch.
- Recommender Engine - Under The Hood - Feb 21, 2018.
We examine two main types of recommender systems: Content based and Collaborative filtering. Both have their pros and cons depending upon the context in which you want to use them.
- Recommendation Engines and Real-time personalization – download guidebook - Oct 26, 2017.
Recommendation engines are effective because they expose users to content they may not have otherwise found. For a step-by-step guide on building an effective recommendation engine from the ground up, check out our latest guidebook.
- The Data Science of Steel, or Data Factory to Help Steel Factory - Apr 25, 2017.
Applying Machine Learning to steel production is really hard! Here are some lessons from Yandex researchers on how to balance the need for findings to be accurate, useful, and understandable at the same time.
- Cartoon: When Self-Driving Car + Machine Learning takes you too far … - Jan 4, 2017.
What can happen in the not too distant future when advanced technologies like a Self-Driving car and Machine Learning Recommendations Engine are combined ...
- DataRPM: Building Data Products For Recommendations And Predictions, Webinar, Feb 18 - Feb 16, 2016.
Learn how to build data products for recommendations and predictions quickly and easily using DataRPM; Why productizing data can transform your organization.
- Prediction.io open source machine learning server - Apr 10, 2014.
Prediction.io is an open source machine learning server for predictive solutions, such as personalization or recommendations, built on top of scalable frameworks such as Hadoop and Cascading - ready to handle Big Data.
- New Book on Realtime Analytics and Recommendation Engines - Jan 23, 2014.
The book covers realtime analytics and its application to recommendation engines from a control-theoretic perspective.