Tutorial on Open Source Online Learning Recommenders
Róbert Pálovics, Domokos M. Kelen, András A. Benczúr · 2017
Recommender systems have to serve in online environments that can be non-stationary. Traditional recommender algorithms may periodically rebuild their models, but they cannot adjust to quick changes in trends caused by timely information. In contrast, online learning models can adopt to temporal effects, hence they may overcome the effect of concept drift.