One of the surest ways to start down that path of making your data science and machine learning work for you is to find low-hanging fruit. Recommender systems have proven to be one of the most useful applications of data science to the consumer-facing web since the earliest days of the internet. This talk covers why and how one was built to recommend colleges to prospective high school students, the application of popularity tables and collaborative filters, as well as other approaches and the reasons for doing them sparkled with some war stories about their success and failures. Hopefully after this you can find how your data can work for your users to transparently improve their interaction with your websites instead of sitting in the back office somewhere helping some executive add graphs to their TPS reports.


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Cal Evans at 16:30 on 9 Feb 2019

Worth the F* price of admission. :)

Hernan Leon at 17:31 on 9 Feb 2019

Great talk! Enjoyed and got a good high level idea on recommendation engines.

Parth K at 17:54 on 9 Feb 2019

One of the best talks in the conference. So enjoyable and so F'ing awesome as Terry would say.

Brian Johnson at 17:42 on 10 Feb 2019

Wildly entertaining. So glad I attended this talk.

Kat Zien at 21:55 on 10 Feb 2019

This was definitely one of my favourite talks at the conference! I liked how Terry told us about all sorts of things, from explaining how recommendation systems work to Clarke's three laws by incorporating it all into a very compelling story (with a hefty dose of humour and the occasional F bomb). His slides were a demonstration of some top-notch Keynote-fu. An excellent, well-delivered and entertaining talk which gave a lot of insight into the programming community and the history of dating apps among many other things :)

David Bisset at 09:00 on 17 Feb 2019

Nice humor, nice slide design, and it was great meeting you outside of the talk.