Clarence: I am a Security Research Engineer at Shape Security. My role is to go through our data and come up with models to identify traffic and automated attacks on large websites and stop them. These are core competency of our company. We gain these competencies in a variety of ways, but machine learning is starting to become more and more of a focus of the company.

Clarence: In general, the premise is that there will be a recommendation problem to solve (this will not be a complex problem). There won’t be too many different features or dimensions to look at, because I want to focus on the problems that developers face in general area (as opposed to specific to a particular data set).  I will go through, on a high level, the different kinds of recommendation systems that are possible. For example, highly secured recommendations for simple aggregate to tailor individual profiles for users on the platform and then I will go on to content based items recommendation systems, to generate item and user profiles with collaborative filtering to match them up together. Then, I will go into feature engineering on explicit, implicit, and latent features. These are things that I will slowly add on and with just a few lines of code. In each topic that I will focus on, I will add on to this recommended system that we will build throughout this system in our discussion.

Clarence: I think that those coming to the talk would be interested in some kind of recommender systems and have a potential use case for it. They should know some basic terms in the space like ‘what is a recommender system?’ I will go through terms on a high level but it’s hard to compare all the different ways of solving a particular problem. Recommender systems are just one way of solving the general recommendation problems. So if they were to know in general what recommender systems are meant to do, that would be great.