WhizzCo predicts for each page view, which recommendation provider is best
Today, I stumbled upon WhizzCo, a start-up with a great idea. WhizzCo tackles the problem that there is no single-best content recommendation-engine or advertisement platform to monetize your website. Sometimes, Google Adsense may be best, sometimes a service like Plista may be best. WhizzCo promises to analyze for you, which platform generates the most revenue for your website. And, as far as I understand (sadly the website lacks in-depth information), WhizzCo is doing this for every single page impression. This means, for visitor A who looks at page X, one monetizing platform (e.g. Adsense) might be used, but for visitor B who looks at page Z, some other monetizing platform may be used.
This approach is identical to per-instance algorithm selection, and Automated Recommender Systems (AutoRecSys). I have published in this field a bit and founded my own business start-up Darwin & Goliath. As such, I am particularly pleased to see how the field of AutoRecSys starts to gain attention.
According to the website, WhizzCo was founded already in 2017…
… though the LinkedIn profile says it was 2018.
Read more on https://techcrunch.com/2021/04/22/whizzco/.
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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).