When we provide ratings for products and services on the internet, all the preferences we express and data we share (explicitly or not), are used to generate recommendations by recommender systems. The most common examples are that of Amazon, Google and Netflix. In this article, I have combined movie attributes such as genre, plot, director and main actors to calculate its cosine similarity with another movie. The dataset is IMDB top 250 English movies downloaded from data.world. Ensure that the Rapid Automatic Keyword Extraction (RAKE) library has been installed (or pip install rake_nltk).

It's time to revisit the discussion on recommendation engines. In this installment, I'm going to provide you a conceptual overview of the topic, and then, following that I'll show you how to build a recommendation engine in R. Ready? Before showing you how to build a recommendation engine in R, I need to get you up-to-speed on the concepts behind how recommendation engines work. In case you're totally new to marketing data science, let me illustrate the recommendation engine concept a little before proceeding. You know how, when you go buy something on Amazon, you see related products under the heading of'People who purchased this item also purchased…' (or something like that).