[shadowmint]

Obviously ML can add a lot of value here, but its questionable to me if its trivial to build such a model with available data, keep said model up to date, or train variations on it easily, cheaply and quickly enough to A/B test the result and ensure you’re actually making any tangible difference.

So you know... I don’t think it’s unfair to say that for smaller vendors, the cost/effort of setting up a ML model may dwarf the fractional improvement it offers over just having one person doing human generated SQL queries.

The point is this isn’t like machine vision or voice, where its almost expontentionally better than traditional approaches.

It’s just... a bit better. Which is worth it only if the fractional improvement pays for the setup cost.

[halflings]

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...

[natalyarostova]

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.

[kennu]

All the big cloud providers are offering pre-trained models for currently popular AI/ML use cases, such as image labelling, face recognition etc. I think this will be the easiest way to apply AI/ML, combined with transfer learning so that the provider can pre-train the basic model and then provide a way for the customer to customize it further for a specific use case.

Of course this can also fail if the pre-trained generic models don't offer enough value and you end up having to develop your own models, but we'll see how it goes.

Btw, I've published a short Kindle book that aims to provide an overview of these pre-trained services currently available on various clouds, it can be found on Amazon by searching for AI ML Managed Services 2018. It attempts to save you the trouble of scanning through all the online documentation to find out what they do.

[wodenokoto]

But the SQL approach at best only answers which item type to present the customer.

How does the query make a good decision on the specific item?