Search on Harlan D. Harris

Search Describes Solutions -- But What About Problems?

harlan.harris@gmail.com (Harlan Harris) — Mon, 20 Jul 2026 00:00:00 +0000

In a previous post I said that Search User Interfaces (UI) have two jobs -- helping people find things and educating them about the catalog's structure. (I'll use "catalog" here under the assumption that I'm describing an e-commerce search system, but much of this post is relevant for other search domains.) The search results, facets, and other elements describe the catalog: the solutions and products available.

But there's a third job that's often not addressed and not always well defined: helping users understand their problem, before they're ready to evaluate solutions. Standard search UI patterns assume the user is already in the right mental state to evaluate what's on offer. The design challenge that I want to discuss, then, is to figure out how best to support problem education in search interfaces.

A Big Win from a Small Search UI Change and a Predictive Model

harlan.harris@gmail.com (Harlan Harris) — Thu, 09 Jul 2026 00:00:00 +0000

One of my biggest commercial wins was a seemingly small UI tweak that led to a roughly 1.5% marketplace revenue bump. Teachers Pay Teachers (TPT, think "Etsy for Teachers" -- educational resources created by and sold to other educators) was a mature marketplace, where revenue wins from search system changes were typically under 0.5%. How we got this win is, I think, instructive, reinforcing the value of a search scientist, and of having a holistic view of search UI/UX and algorithms.

Design Decisions for a New Search Results Page

harlan.harris@gmail.com (Harlan Harris) — Fri, 08 May 2026 00:00:00 +0000

One of my specialties as a freelance consultant is discovery systems for e-commerce -- web pages and backend systems for helping users search for and find what they want or need. For the last year and a half, I've been working as a consultant for DesignShop, a company that sells materials samples for home renovations. Want to find a countertop, cabinet, paint, and tile combo for your kitchen reno that looks great, with samples shipped overnight? Check 'em out.

You Don't Need a Search Engineer; You Need a Search Scientist

harlan.harris@gmail.com (Harlan Harris) — Mon, 13 Apr 2026 00:00:00 +0000

Say you're an Engineering Manager or a Product Manager, responsible for a business-critical search system on a website. Maybe e-commerce, maybe a marketplace, maybe something else. Search is sort of working, it doesn't crash, and it's fast, but there's a feeling that "search isn't great", and you've been tasked with finding someone to help. Your first instinct, or that of your VP, might be some version of: “we need to hire a search engineer.”

IZE meets AI -- semantic search, smarter labels, and agentic orientation

harlan.harris@gmail.com (Harlan Harris) — Wed, 18 Mar 2026 00:00:00 +0000

This is the last post in the IZE series. In the previous installment, I looked at two ways to generalize the IZE algorithm itself: preferring consistent facets and searching for trees with better goodness scores. Here I want to ask a different question: what does the AI revolution of the last few years actually change about what could be built here?

I see three distinct opportunities, at different layers of the system.

GeneralIZE -- How else could IZE's hierarchies be generated?

harlan.harris@gmail.com (Harlan Harris) — Tue, 17 Mar 2026 00:00:00 +0000

In previous posts, I showed off an interactive demo of the IZE algorithm, and discussed how the algorithm worked. Now, it's worth considering some ways we could generalIZE the algorithm. 🤦‍♂️ Perhaps variations on the algorithm might yield hierarchies that are even better at showcasing the contents of the texts?

As discussed previously, there's pretty good experimental evidence that in order to be useful and intuitive, search results clustering needs to be based on just one or two words (or facet values, or tags). That is, it's too confusing when the algorithm finds clusters based on overall family resemblance, then tries to name them post-hoc. It's better to do the sort of single-word splitting that the IZE algorithm uses. But, could we do so in a different way or a different order, so that the results feel more like underlying clusters are being identified?

Could We Build IZE Again?

harlan.harris@gmail.com (Harlan Harris) — Mon, 09 Mar 2026 00:00:00 +0000

In the previous installment of this series, I looked at what came after IZE -- faceted search, clustering algorithms, and the various ways web search, personal information management, and e-commerce tried to solve similar problems to what IZE was attacking. None of them ended up doing what IZE did. The question I want to take up here is: could we build something like IZE today? I think the answer is yes, for at least one domain. IZE's hierarchical search algorithm is worth revisiting for e-commerce operators with medium-to-large catalogs who want to help new customers orient themselves. Modern catalog metadata makes it easier to implement than ever.

What Came After IZE? Three Domains, Three Answers

harlan.harris@gmail.com (Harlan Harris) — Wed, 04 Mar 2026 00:00:00 +0000

In the previous post in this series, I discussed the technical details of IZE and its reception. Here I want to look at what came after — and where IZE-like ideas might still have potential.

The short version: IZE was forgotten, but the ideas it embodied — hierarchical clustering, single-word splits, dynamic navigation — were re-invented independently in several different domains. Each domain found a different answer, for reasons that are worth understanding.

How IZE Really Worked - Algorithm, Patent, Limits, and Esther Dyson

harlan.harris@gmail.com (Harlan Harris) — Sat, 28 Feb 2026 00:00:00 +0000

In the first post in this series, I introduced IZE -- a DOS-era personal information manager with a novel approach to search and navigation. Here I want to go deeper into how it actually worked, what its limits were, and how it was received at the time.

The algorithm

The core of IZE was patented by Paul Kleinberger (US5062074A, "Information retrieval system and method"). The basic algorithm is easy enough to describe:

IZE - Revisiting a hierarchical search technology from the PC era

harlan.harris@gmail.com (Harlan Harris) — Fri, 27 Feb 2026 00:00:00 +0000

Sometimes revisiting old technology is the best way to understand how we got where we are -- and to see what alternative paths might have looked like. This is the first in a series of posts about IZE, a DOS-era personal information manager that I think has some interesting lessons for modern search and discovery.

I'll admit my interest in IZE isn't purely academic. My father was a co-founder of the company that published it, and a cousin of his was the inventor of the underlying technology, which the company acquired. So I grew up with IZE around the house, and owned the polo shirt. That said, I think it's genuinely interesting on its own merits, and I'll try to make the case for that here.

11 Algolia A/B Testing Gotchas, Tips, and Lessons!!

harlan.harris@gmail.com (Harlan Harris) — Mon, 09 Feb 2026 00:00:00 +0000

I've supported A/B testing of Algolia search systems at three companies now, and have dived deep into A/B testing generally as well as specifically for search. The Algolia documentation on search A/B testing is technically adequate for getting started, and the dashboard has improved, but there are still many ways that you can go wrong when A/B testing Algolia search results. In the style of a 2014-era Buzzfeed listicle, here are 11 Algolia A/B Testing Gotchas, Tips, and Lessons!! All of the horrible illustrations are generated by AI, the rest are from earlier posts.

How Search and AI Product Teams are Different

harlan.harris@gmail.com (Harlan Harris) — Tue, 03 Feb 2026 00:00:00 +0000

Engineering leadership knows the standard playbook for product teams: the Product Trio, the Spotify model, outcome-based roadmaps, and so on. I've seen teams adopt these practices and still struggle when they add responsibility for advanced algorithms -- search, recommendations, predictive modeling, or generative AI -- without changing how the team is led. The processes and role expectations that work for a typical product team break down when the domain is heavily algorithmic. This post pulls together what I've learned and what others have written about building teams that own these systems.

Post Query Refinement Suggestions in Search UX, and an Algolia Demo App

harlan.harris@gmail.com (Harlan Harris) — Fri, 23 Jan 2026 00:00:00 +0000

What are Post-Query Refinement Suggestions?

One of my favorite search UX patterns is post-query refinement suggestions — buttons that appear between the search box and results, adjusting the query in various ways. See, for instance, these suggestions on Etsy, which recommend that I filter by shipping speed, seller type, cost range, style, etc.

Filter Suggestions on Etsy

PQRS is a mouthful of an abbreviation, but most of what I've seen as alternatives either describes the UI element ("refinement pills") rather than the function, or is excessively vague ("guided search"). So PQRSs it is, sorry.