[mlthoughts2018]

It varied depending on the domain. In one company it was a high traffic online merchant, and customers were basically a random sample of the middle-class population at large, no better-than-average-person computer literacy.

In another case it was a multimedia company and the search users were producers, artists and technical directors, with much more of a power user mentality to searching for what they wanted, and jobs that required basic computer literacy and maybe familiarity with special GUI programs.

But I’m telling you, in every focus group or A/B test or user session we ever looked at, at any of these companies regardless of the user characteristics, the absolute number one thing every time was user requests for more and more categorical filters to allow them to efficiently exclude huge classes of results they know are not relevant to them.

Amazon (I did not work there) actually gets this right, with lots of brand, size, color, Prime-eligible, etc. filters for everything. That stuff requires no machine learning, is simple to implement and A/B test and measure usage efficacy for, and people really want it.