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 being actively developed anymore. This is a pity, as Surprise is easy to use (I always recommend it to my students for their first experiments). As a side note, my group recently released ‘Auto-Surprise‘, an AutoML-like extension to Surprise.
A prototype of a “Similar Shoe” (Sneaker) Recommender-System with Source Code [Josh Barua]
A Case For Embeddings in Recommendation Problems [Arnab Bhadury]
Build a Recommender System in less than 60 minutes with ‘Amazon Personalize’ [Shivek Sachdev@Medium]
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).