This chapter showed you how to apply the techniques in a real-life context. Starting with raw unstructured data, we built a rating matrix, which is the input of collaborative filtering. In addition, we extracted the item description, which improved the performance of our model. Using performance evaluations, we optimized the model parameters. The same approach can be applied in real-life contexts, if properly refined.
This book is a path that shows, first, the basics of machine learning and then a practical application. After having read this book, you will be able to deal with real-life challenges, identifying the most appropriate recommendation solution. Thank you for following until this point.
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