TorchRec, a new Recommender-Systems Library by PyTorch
The PyTorch team (and Meta.ai aka Facebook AI team) announced a new software library for recommender systems: TorchRec (GitHub). PyTorch is one of the major Deep Learning libraries, besides TensorFlow and Keras. As such, the announcement of TorchRec is big news for the recommender-systems community. If TorchRec is as powerful for recommendations as is PyTorch for ‘general’ Deep Learning tasks, the library could be very beneficial for recommender-systems developers. We added the library to our list of recommender-systems libraries, and hope to see the first reports on its effectiveness soon.
OpenAI makes many of its AI models available through an API
Scalable Recommender Systems with NVTabular- A Fast Tabular Data Loading and Transformation Library [Ronay Ak @ Medium]
Google AI releases ‘RecSim NG’, a “Flexible, Scalable, Differentiable Simulation of Recommender Systems”
About The Author
Joeran Beel
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).