> Metarank is industry-agnostic and can be used in any place of your application where some content is displayed.<p>I'm afraid I'm skeptical.<p>Content ranking in small, well defined contexts is not hard to do and doesn't require an ML approach – rules based systems are often easier to specify, easier for both creators and users to understand, and easier to make conform to business rules.<p>When ML does need to be introduced, when the scale or complexity is large enough that a rules-based approach will be infeasible or worse, having a generic implementation is unlikely to return useful results. So much of the work of optimising an ML approach is engineering features out of the data that make sense and that don't introduce bias.<p>It's that last point that's really important because if you do the wrong feature engineering, then the bias introduced effectively means you're back to building a rules-based system, just one that has a bunch of inaccuracy built in, and where you don't understand what rules you've specified, or even that you have specified them.<p>I'm not an expert here, but I've worked on basic recommender systems for products, and worked with people who were far more knowledgeable about this, all of whom seemed to have a low opinion of generic systems.