Collaborative filtering

Goldberg, Nichols, Oki & Terry · 1992

Recommend to someone what people who behaved like them went on to choose, without needing to know anything about the item.

Its place in the frameworkOmni Core›Personalization

What it does

The insight is that similarity of behaviour carries more information than description of content, and it needs no understanding of what is being recommended at all. Two problems have followed it ever since: it has nothing to say about a new user or a new item, and it narrows what people are shown to a reflection of what they already did.

Reach for it when: Where there is enough behavioural data for patterns to be real, and as the standard against which content-based approaches are judged.
Where it stops: It amplifies the popular and the already-chosen. Left alone it will recommend a company’s catalogue into a small corner of itself.

David Goldberg, David Nichols, Brian M. Oki & Douglas Terry, “Using Collaborative Filtering to Weave an Information Tapestry”, Communications of the ACM, 1992.

Why it sits at Personalization

Changing what a customer sees based on what is known about them — deliberately, within limits, and only where it demonstrably helps them rather than only the organisation.

A model is only useful when you reach for it at the right moment. This one answers a question that arises here — so it is filed here, and nowhere else. These are the working areas it serves:

What is personalisedRecommendation-system practice, and the distinction between personalisation that reduces effort and personalisation that increases exposure.
The signalsWork on inference from behavioural data, and the error rates of attribute inference that customers experience as being misunderstood.
The limitsPrivacy-calculus research on the trade customers make between disclosure and benefit, and where it breaks down.
Whether it worksControlled testing applied to personalisation, and the frequency with which untested personalisation shows no effect.

All of Personalization

What it touches elsewhere

Nothing in a business is decided on its own. A conclusion reached with this model at Personalization lands in these other cores, whether or not anyone follows it there.

Data CorePersonalisation runs on collected data, and the purpose it was collected for constrains this use.
Brand CorePersonalisation that misjudges someone damages perception more than a generic experience would.
Market CoreSegments are defined there; personalisation is what is done differently for each.
Business CoreEvery personalised variant is something to build, maintain and get wrong.

Filed at the same place

These answer questions that arise at Personalization too. Where they disagree with this one, the disagreement is the useful part.

RFM analysisSort customers by how recently they bought, how often, and for how much — three fields that predict a great deal.

Elsewhere in Omni Core

These are other people’s models, named here so you can go to the source and use them properly. The Omnigoal is not affiliated with their authors and is not endorsed by them; nothing of theirs is reproduced here — no canvas, no diagram, no wording. Each is described in our own words, with the originator credited, because the framework is a place to put thinking, not a replacement for the people who did it. Model names and trademarks belong to their respective owners and are used here only to refer to the work itself.

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