Google Scholar re-designs its research-paper recommender system
Google Scholar re-designed its recommender system as announced by Namit Shetty, Alex Verstak, Kyu Jin Hwang, Linghua Jin, Philippe David, and Anurag Acharya.
Your Scholar Recommendations just got better – fresher, more relevant, and easier to scan. If you have a Scholar profile and are actively publishing, your Scholar homepage should have recommended articles that look like this:
https://scholar.googleblog.com/2021/02/scholar-recommendations-reloaded.html
A prototype of a “Similar Shoe” (Sneaker) Recommender-System with Source Code [Josh Barua]
Microsoft Starts New ‘Because You Liked…’ Game Recommendation Feature For Xbox Game Pass [Laurent Giret @ OnMSFT]
Google Podcast gets Personalized Audio News Recommendations [Liz Gannes @ Google]
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).