Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation [Guest Post]
In the last decade, Recommendation Systems (RSs) have gained momentum as the pivotal choice for personalized decision-support systems. The recommendation task is essentially a retrieval...
Experience of using CORE Recommender – an interview
The CORE recommender is recommender-system-as-a-service and API for research-paper recommendations (similar to my own service Mr. DLib). CORE has an impressive open source repository of...
Google AI releases ‘RecSim NG’, a “Flexible, Scalable, Differentiable Simulation of Recommender Systems”
One or two years ago, Google AI released RecSim — A Configurable Simulation Platform for Recommender Systems. Now, Martin Mladenov from Google AI announced the...
‘Spotify Confidence’: convenience wrappers around stat models various functions
Spotify released Spotify Confidence, which provides “convenience wrappers around stats model’s various functions for computing p-values and confidence intervals. With Spotify Confidence, it’s easy to...
Google Introduces “Model Search”, an Open Source Platform for Finding Optimal ML Models
Hanna Mazzawi and Xavi Gonzalvo from Google AI announced “the open source release of Model Search, a platform that helps researchers develop the best ML models,...
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...
‘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...
‘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...