Learn to build a recommendation engine with Content–Based filtering
Build a strong foundation in Content–Based Recommendation Systems with this tutorial for beginners.
Understanding of recommendation systems
Types of recommendation systems
Stop words removal
Cosine similarity algorithm
User Jupyter Notebook for programming
A Powerful Skill at Your Fingertips Learning the fundamentals of a recommendation system puts a powerful and handy tool at your fingertips. Python and Jupyter are free, easy to learn, have excellent documentation.
Jobs in the recommendation systems area are plentiful, and learning content–based filtering will give you a strong edge. Content–based filtering has the advantage of recommending articles when you have a new app or site, and there are no users yet for the site.
Content–Based Recommendation Systems are becoming very popular. Amazon, Walmart, Google eCommerce websites are a few famous examples of recommendation systems in action. Recommendation Systems are vital in information retrieval, upselling, and cross–selling of products. Learning Collaborative filtering with SVD will help you become a recommendation system developer who is in high demand.
Big companies like Google, Facebook, Microsoft, Airbnb, and Linked In are already using recommendation systems with content–based recommendations in information retrieval and social platforms. They claimed that using recommendation systems has boosted the productivity of the entire company significantly.
Specification: Learn how to create content based hotel recommendations