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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.