  In this industry talk at ECIR'2022, we illustrate how to build a modern
recommender system that can serve recommendations in real-time for a diverse
set of application domains. Specifically, we present our system architecture
that utilizes popular recommendation algorithms from the literature such as
Collaborative Filtering, Content-based Filtering as well as various neural
embedding approaches (e.g., Doc2Vec, Autoencoders, etc.). We showcase the
applicability of our system architecture using two real-world use-cases, namely
providing recommendations for the domains of (i) job marketplaces, and (ii)
entrepreneurial start-up founding. We strongly believe that our experiences
from both research- and industry-oriented settings should be of interest for
practitioners in the field of real-time multi-domain recommender systems.
