3 Different Approaches to Automated Recommendation (Pandora, Strands, Aggregate Knowledge)


In the video above, Chris Law mentions Pandora, and explains how the music site is laser-focused on the DNA of the music files, a technology that enables them to recommend songs to their users based on structural data.

Together our team of fifty musician-analysts has been listening to music, one song at a time, studying and collecting literally hundreds of musical details on every song. It takes 20-30 minutes per song to capture all of the little details that give each recording its magical sound – melody, harmony, instrumentation, rhythm, vocals, lyrics … and more – close to 400 attributes! (Pandora, about page)

This project is called the Genome Project (consult the Human Genome Project, and apply the principles for music). Basically, Pandora’s technology is based on a deep structural analysis of music files. It detects subtle musical patterns, and organizes those patterns into groups, hence the ability to recommend other songs based on the structure of one initial song.


Strands is another recommendation engine, which technology is based on social behaviors. Strands’ goal is to personalize your online experience, by understanding who likes what, and generating suggestions based on the social feedbacks of users (a technological approach very similar to FFWD‘s video technology).

Strands develops technologies to better understand people’s taste and help them discover things they like and didn’t know about. Strands has created a social recommender engine that is able to provide real-time recommendations of products and services through computers, mobile phones and other Internet-connected devices.“(Strands, about page)

Strands deploys its technology through several different products: Finance, Social Media (mostly music and video items) and Business (helps people discover the content on your site). By mixing people’s likes and dislikes in their tech blender, Strands offers a powerful recommendation algorithm for their users, making them one of the leaders in recommendation technologies today.

aggregate knowledge

Aggregate Knowledge is yet another way to approach the question. In this post, Chris Law explains that his previous experiences taught him that the tastes of your friends usually poorly reflect yours. This is why Aggregate Knowledge is more focused on analyzing the context of the visit (traffic source, landing page, semantics, visitors’ demographics?…) and the behavior of the visitors (page views, clicks, time spent…).

From there, they run multiple algorithms on their servers’ blender and extract the best recommendation possible, to serve it up nice and fresh on their clients’ Website. As it says on the company profile on Crunchbase:

The word in Silicon Valley is that they are doing one hell of a job for their partners, which include the Washington Post and Overstock.com

To conclude, we have 3 different approaches:

  • Pandora: deep structural analysis of an item
  • Strands: intensive social behavior analysis around an item
  • Aggregate Knowledge: structural analysis of an item, paired with behavioral analysis around the item

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