It's not unheard of to see +10-30% in revenue when adding a recommender system [0]; The system described by the author is arguably more complex than a recommender system, since he has to develop, maintain and evaluate a set of rules that are not based on real data, but only on his intuition of what users want. GP gave good examples of how this would easily fail (do you always want to recommend items from the same category; if not, how do you know which other category to recommend?)
For newer or smaller firms the SQL approach makes sense. Both because there is less data, and it's less risky to implement. Once the context is fully fleshed out, then it's easier to move on to a ML recommender system. It's also easier to track improvement vs a benchmark.
Comments
It's not unheard of to see +10-30% in revenue when adding a recommender system [0]; The system described by the author is arguably more complex than a recommender system, since he has to develop, maintain and evaluate a set of rules that are not based on real data, but only on his intuition of what users want. GP gave good examples of how this would easily fail (do you always want to recommend items from the same category; if not, how do you know which other category to recommend?)
[0] http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.895...
For newer or smaller firms the SQL approach makes sense. Both because there is less data, and it's less risky to implement. Once the context is fully fleshed out, then it's easier to move on to a ML recommender system. It's also easier to track improvement vs a benchmark.