From the course: Building Recommender Systems with Machine Learning and AI

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Our recommender engine architecture

Our recommender engine architecture - Python Tutorial

From the course: Building Recommender Systems with Machine Learning and AI

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Our recommender engine architecture

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- [Instructor] So we've got this nice code to evaluate recommender systems and we intend to try out quite a few different ones throughout the rest of this course. What we need now is a framework to let us easily experiment with new recommender system algorithms, evaluate them and compare them against each other. Surpriselib provides us with some of what we need, but we're going to build on top of it. Let me show you the various pieces we're going to use and how they fit together. Surpriselib has a Python based class called AlgoBase. If you don't know what a base class is, then you're probably new to object oriented design. It's an easy concept though. Fundamentally a class is just a collection of functions organized under some name. That class can also have variables associated with it. And you can instantiate an instance of a class object, which lets you keep around a copy of a class, its functions and the data and its…

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