Facebook figures out people that you might already know, LinkedIn tells you how many degrees of separation there are between you and the CEO of Nokia, and LastFM suggests music based on your current listening habits. We’ll take a look at the basic theory behind how some of these features work (no comp. sci. degree required!), and show how you can implement some of these features in your application.


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This was an awesome talk. It got me very interested in the algorithms that drive the social web, which I always thought were completely nebulous and intangible before. Well. They are still nebulous and intangible, but at least I know why now.

Nice job of explaining complex graph theory in terms that the average developer can understand.

Awesome! Talented presenter (you're good at that man!). Eye/mind opening presentation. Went a little bit fast on Neural nets (forget about the neural label for a moment, those things delivered interesting stuff). Thanks again

Excellent talk explaining basic graph theory concepts very well. You should consider going into more details on graph algorithms and their practical use, and extracting the machine learning part into a separate talk.

Thanks for all the comments everyone! I'm glad to see that most people found the presentation useful and interesting. I'll be sure to try and expand on more ANN-specifics in the next iteration of this talk; I agree that I could be a bit less biased against them ;-).

Great talk, deserves 5 stars for sure.
A good improvement point would be more real world examples and some known Graph navigation algorithms, like Dijkstra, Topological Sorting and AI pseudo codes for searching, mainly the used ones for recommendation, such as A*.