How to Train Your Personalization Dragons

Posted by & filed under on May 1st, 2013.

I love this post by Joshua Fruhlinger, Please don’t personalize me. I know who I am. It captures a sentiment that many of us share (1) (2) (3). Joshua provides a wonderfully non-technical and simple summary:

I know who I am. I don’t need Facebook or Google or Microsoft or Apple or anyone else to collect data and tell me what I’m interested in. I’m pretty sure I know what I like and don’t like.

It’s a great point: Not only are big data approaches to personalization privacy-invading and offensive, they don’t work very well!

dragons-1There are much more direct and transparent ways to approach the opportunity of personalization. A system that allows you to be the master of your interests is self-empowering.

How Primal Slays Your Personalization Dragons

So why should Joshua (or anyone who cares about personalization and privacy) care about Primal’s unconventional approach to personalization?

The first reason you should care is it actually works. The information is personalized to your interests, directly and unambiguously.

The screencap below shows an area of my interest network around the topic of entrepreneurship.

dragons-2
The topics are drawn from my LinkedIn profile, along with a number of related topics of interest that Primal’s AI identified. (The full interest graph for this set of inputs involves thousands of related terms; this visualization presents a small subset of the data.)

dragons-3

The result is an interest graph that describes your specific interests. It’s as unique and one-of-a-kind as the collection of topics used to create it. The content that’s organized by your interest graph is highly targeted and relevant, so you don’t have to spend a lot of time sifting through the noise.

The second reason you should care is that you can actually understand Primal. With many competing approaches, not even the developers implementing the solutions truly understand how they work, let alone the people that these solutions are intended to serve.

Unlike these mysterious, black-box approaches, Primal is accessible to end-users. You can directly affect your interest graph (and the personalized experiences your interest graph powers). You don’t need a team of “data scientists”; you only need to express yourself using collections of keywords, in the same way you do today.

Which leads to the third reason to care: Primal puts you in complete control. If you want an interest represented in your graph, just add it; if you don’t, delete it. This is your interest network.

To Joshua’s point, you know who you are and what you’re interested in. With Primal, you can have all the power of a personalized experience without compromising your control or privacy.

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