chapter four
4 Content-Based Recommendation
This chapter covers
- An overview of the most common recommendation techniques
- How to design proper graph models for a content-based recommendation engine
- How to import existing (not-graph) datasets in the graph models designed
- How to implement working content-based recommendation engines
Representation is one of the most complex and compelling tasks in machine learning, and computer science in general. Pedro Domingos, a CS professor at the University of Washington, published an article in 2012 [Domingos, 2012] in which he decomposes machine learning into three main components: representation, evaluation, and optimization.
Representation, specifically, affects three core aspects of a machine learning project’s lifecycle:
- The formal language (or schema) in which a training dataset is expressed before passing it as input to the learning process
- The way in which the result of the learning process—the predictive model—is stored
- How, during the prediction phase, the training data and the prediction model are accessed during forecasting
All of these are influenced by the learning algorithm used to infer the generalization from the observed examples in the training dataset, and they affect the overall performance in terms of forecast accuracy and training and prediction performance (speed).