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Experience of using CORE Recommender – an interview
June 8, 2021
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 over 25 millions full-text articles and the recommender system aims at digital libraries and other academic services.
The CORE team recently did an interview with George Macgregor, Scholarly Publications & Research Data Manager at the University of Strathclyde about how useful CORE is. While the interview is clearly a bit advertisement-heavy, it is an interesting read for those being interested in research-paper recommender systems. Among others we learn that the University of Strathclyde observed a 58% increase in the length of time unique visitors spent on Strathprints following implementation of the CORE recommender system.
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).