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That's true, I'm making recommendations based on Multinomial Naive Bayes (and SGDClassifier) over custom TF-IDF bags of words, so it is like search plus text classification. And some endpoints do just check the cosine or Jaccard distance between things. There is a lot of overlap between search and NLP.

My approach to AI is somewhat conservative because of working in a law-adjacent field where explainability is paramount. When it comes to getting "smart" I prefer forward-chaining logic over facts, and facts include predictions from models too. But at least there is a "judge"/engine to coordinate how the predictions from the ensemble of models maps to actions. I love me some pertained neural nets, but use them more as black box appliances.

[krasi0]

Which forward-chaining engine do you use? Something based on prolog?

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Currently https://github.com/nilp0inter/experta but https://github.com/noxdafox/clipspy seems nice, I just shied away from using it due to uneasiness about FFI and debugging, even though the original CLIPS is still awesome and has a very interesting manual.

There's also https://github.com/jruizgit/rules but haven't tried it yet.

[krasi0]

Interesting. I guess your driving factor was that you can use those directly from Python. How's the performance (with many many rules)?

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Yes exactly, I want to be able to do rich auditing of the predictions. Not sure about performance yet, still prototyping!