Chris Law is the co-Founder and CEO of Aggregate Knowledge, a Web startup that specializes in serving recommendations on medium and high-traffic sites. Previously, Chris launched Tribe.net, an early social networking site. That social graphs are poor predictors for advertisers is the major lesson he learned from this startup.
Aggregate Knowledge doesn’t tap into social graphs to serve recommendations to its visitors. Instead, it uses a complex algorithm that analyzes two major dimensions of the visitor:
- Behavioral patterns
- Contextual patterns
The behavioral analysis is anonymous, so it doesn’t raise the same issues Beacon did. The contextual analysis is based on semantics. By mixing those two ingredients, Aggregate Knowledge serves up quality recommendations to its clients, who just have to insert a snippet of code in their site’s sidebar to get the service up and running. Aggregate Knowledge is a good example of a startup which development plans drift away from the social hype of the Web 2.0. In this post, Alex Iskold describes the challenges of building a recommendation engine. Since he is in the recommendation business, he has great analytical skills on that subject, and the post makes us understand how complex designing a behavioral/contextual recommendation engine can be.
In the same spirit of heavy data storing and crunching, Aggregate Knowledge’s approach is complex and powerful: Both Google and Microsoft invest heavily in behavioral targeting technologies, and semantics has been publicized as the new big trend many times (some competing recommendation engines focus exclusively on semantics).
However, Aggregate Knowledge isn’t a search tool, but a discovery tool. Discovery happens in a different context than search, a topic I will further expand on in the next post.
Read related items:
Comparing Discovery Tools (Whonu, Evri, Aggregate Knowledge)
3 Different Approaches to Automated Recommendation (Pandora, Strands, Aggregate Knowledge)
Recommendation Engines: Future for Retailers and Content Providers?