The internet knows no horizon.  It is the most visible example of technology’s unstoppable progress and is so vast that we couldn’t cope if we had to use pre-Google search engines. Google’s Page Rank algorithm admirably filters signal from noise, but the imminent commoditization of production and the consequent explosion choice presents a new challenge: will an algorithm be able to make our purchasing decisions for us?

Dom explained the problem in an earlier post:

…the major challenge for new companies will not be in sales or manufacturing, but bringing their goods to the attention of buyers. The challenge for the consumer will be focusing their attention on the right goodsWhen everyone’s mom, sister, and brother is a producer, we’ll need to filter the deluge of brand messaging else expect to washed away in the mass of creativity.

In this post, I’ll begin to explore the solution.

Collaborative Filtering

The classic computer algorithm for the problem of recommendation is a technique called Collaborative Filtering. It’s the formalisation of a simple idea; if we know people’s past purchasing behaviour (including yours), then we should look at what’s popular with people whose purchases mirror yours. The amount that you are likely to like an item X is the average of how much everyone else likes X, weighted by their similarity to you.

This technique might seem very crude, but like many data-mining algorithms, predicts your preferences quite well due to the massiveness of the datasets involved. Amazon famously implemented a ‘reverse’ version of it (using users’ purchasing patterns to link similar items) to produce the ‘Customers who bought X also bought Y’ section of their website.

There’s no doubt this approach may lead us to discover interesting new products, or prompt us to buy something that we’ve forgotten we wanted. However, discovery is becoming easier as the long tail of products comes to the fore. Does the algorithm really help us to decide which product is best for us? Does it give us confidence that we are looking at the perfect choice? Can a machine help us own the right stuff?

The User Review

A human alternative to a machine recommendation is a user review. Pioneered by Consumer Reports and deftly wielded by the Amazons and Yelps of the world, the user reviews’ achilles heel lies in it’s propensity to polarize opinion. People review products if they love or hate them [1], but rarely if they are ‘just OK’. Contrary to what it appears online, it’s the silent majority between love and hate that actually BUY things.

Moreover, out of context, it can be very difficult to tell if someone’s opinion can be trusted. Try this experiment: think of your favourite product, find it on Amazon, and have a look at the negative reviews (there’ll be some!). Would you know, uninitiated, that those reviewers were dead wrong? Or at least that their opinions were baseless? Product reviews can range from nitpicky rambling loud noise to compelling argument.


The issue in both cases is trust. It’s hard to trust the computer, because we don’t know what it’s thinking. This is less of a problem when we are deciding on a movie, but when the outlay of money is higher, it becomes a more serious issue. Likewise, when we read a review, we generally just don’t have enough of a handle on the reviewer to know if we can trust them.

The solution to the trust issue online is simple: context. If we know a user’s pattern of behaviour, it’s easy to understand the significance of a single act. The idea of a bindle is to provide that context with a single glance. It’s the easiest way to see in a moment how the recommender’s choice of a product fits within the general pattern of their choices.


Seeing the kind of bikes those wheels are used on might make you realise they are pretty pro.


Seeing this Apple-device user with a Nexus One tells you something about that particular phone.

The advent of the internet has dragged us into a post information age; attention is the new scarcity. The latest trends on the web revolve around curating the firehose of information that is available.

Bindle is an effort to enable us to trust other gear lovers, driving that trust by allowing us to see someone’s choices in the context they have been made. Everyone has friends who they trust for their excellent sense of style, or technical knowledge, or experience in some area. I know there are people who’s recommendations I would blindly buy without further thought. Bindle will enable this behaviour on an internet scale.

We hope that in the future, with Bindle, we will all own the right things, thanks to the recommendations of those we trust.

  1. they also tend to love or hate things more than they should []
Tom Coleman

Co-creator of, searching for simplicity, quality and elegance in technology, products and code.

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