How are you going to allow folks to influence the system? Or do you see your system integrated behind their pseudo-recommendation engine?
The other related market trend we think about here: recommendation is going through a similar journey to what search did 10 years ago. Search at some point was more build leaning, but over time the technology became democratized and then companies like Elastic and Algolia had offerings that pushed search to lean towards buy. We're seeing recommendations going through the same revolution now that the technologies and system design (e.g. 4 stage recommenders) are more solidified. It's the data that makes these systems unique between companies not the infrastructure or algorithms.
Setting the objective function is often the most challenging. Different teams may prioritize different objectives and often it requires balancing multiple at once! For instance, how does a company think about the types of user engagement and long-term metrics like retention? A model optimized for clicks might be worse for retention in some cases, but not in others. Ultimately, we A/B test to find out. Surprises and counter-intuitive results are common!