How to identify the most relevant recommender systems? Recommender systems, such as "customers who bought this item also bought," are omnipresent on the internet and play a vital role in the online consumer purchase decision. Single web pages typically offer many recommender systems in parallel. The vast variety of decision-making systems in use is driven by technological possibilities. Space constraints arise with the continuously increasing number of available recommender systems and are exacerbated by smaller screen sizes on mobile devices. The crucial question becomes how to implement only the most relevant recommender systems, yet this question still awaits a comprehensive answer. This dissertation takes up the challenge. It shifts from the software engineer perspective of creating one-size-fits-all solutions to the business perspective of managing choice.