The document discusses machine learning and recommendations. It provides an overview of Mahout and how it can be used to build recommender systems. Specifically, it explains how recommendation algorithms work by analyzing cooccurrence patterns in user behavior logs. It then provides a hypothetical example of a working recommender system that collects user history and item metadata, performs cooccurrence analysis with Mahout, and posts results to a search engine to provide recommendations.
Note to speaker: Move quickly through 1st two slides just to set the tone of familiar use cases but somewhat complicated under-the-covers math and algorithms… You don’t need to explain or discuss these examples at this point… just mention one or twoTalk track: Machine learning shows up in many familiar everyday examples, from product recommendations to listing news topics to filtering out that nasty spam from email….
Talk track: Under the covers, machine learning looks very complicated. So how do you get from here to the familiar examples? Tonight’s presentation will show you some simple tricks to help you apply machine learning techniques to build a powerful recommendation engine.