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

Unlock this course with a free trial

Join today to access over 22,600 courses taught by industry experts.

Content-based recommendations and the cosine similarity metric

Content-based recommendations and the cosine similarity metric - Python Tutorial

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

Start my 1-month free trial Buy for my team

Content-based recommendations and the cosine similarity metric

“

about some actual recommendation algorithms. We're going to start with the most simple approach, recommending items just based on the attributes of those items themselves, instead of trying to use aggregate user behavior data. For example, it can be effective to just recommend movies in the same genre, as movies we know somebody enjoys. can make them even better. So let's think about recommending movies just based on the attributes of the movies themselves. The movielens dataset doesn't give us much to work with, but one thing it does tell us is which move genres each movie belongs to. For every movie, we're given a list of genres like science fiction, horror, romance, children's, westerns, et cetera, that might apply to that movie. So if we know a given user likes science fiction movies, it's reasonable to recommend other science fiction movies to that user. Movielens also encodes the year of release into the movie titles, so we can use that information as well. So instead of just…

Contents