Google Chrome’s Update on Privacy Sandbox Initiative (Browser Tracking without 3rd-Party Cookies)
Third-party cookies are were a central component to personalized advertisement on the Web. Due to GDPR, privacy concerns of users, and other reasons, using third-party...
Building a content-based music recommender-system [Amol Mavuduru @TowardsDataScience]
Amol Mavuduru wrote a tutorial on building a content-based music recommender system with Spotify data. Have you ever wondered how Spotify recommends songs and playlists...
Google launches ‘Product Discovery Solutions’ including search and recommendations for online retailers
Google joins the club of companies offering recommendations-as-a-service (RaaS). In a press release, Google announced the launch of “Product Discovery Solutions for Retail, Bolstering Personalized...
‘Rex’: Medium’s Recommender-System as a Microservice [Miles Hinson]
Miles Hinson writes about his experience in developing ‘Rex’, the recommender system for Medium, as a Microservice. He discusses design choices and presents a few...
‘Lazy Predict’ Tutorial for AutoML with scikit-learn [Eryk Lewinson]
Eryk Lewinson wrote a tutorial on how to use Lazy Predict, which is an automated machine learning (AutoML) extension by Shankar Rao Pandala for scikit-learn....
Recommender-Systems Version Control: TensorFlow releases ‘Machine Learning Metadata (MLMD)’
Version control for recommender systems is a topic that should receive more attention in the community. Given that TensorFlow is often used for implementing recommender...
15th ACM Conference on Recommender Systems (RecSys 2021): Call for Papers
ACM RecSys has published the Call for Papers for the 15th ACM Conference on Recommender Systems (RecSys 2021), which will be held in Amsterdam, Netherlands,...
How can you build simple recommender systems with Surprise? [Amol Mavuduru]
Amol Mavuduru wrote a nice tutorial on how to build recommender systems with the Surprise Library. It should be noted, however, that Surprise is not...
‘AutoGL’, a new AutoML framework for Graphs by Tsinghua University
‘Graphs’ and linked data are highly useful in generating effective recommender systems. Also in machine learning, graphs have gained popularity. Now, researchers from Tsinghua University...
HetSeq: Training BERT on a random assortment of GPUs [Yifan Ding et al.]
BERT has brought huge changes to how NLP is done, and also had a notable impact on recommender systems (not always though*). However, training BERT...