usmanziak/movie_recommender_engine

A Content-Based movie recommender engine built using Python and the Scikitlearn library.

Movie Recommender Engine

This Item-Based Movie recommender engine is built using Python's Scikitlearn library and is based on the IMDB 5000 movie dataset. The recommender engine evaluates using CountVectorize and Cosine similarity scores.

What are recommendation systems?

A few decades ago, you would probably rely on your friends, family or experts for advice on what books you should read, movies you should watch, or restaurants you should try. However as we have progressed from the steam engine to the search engine, we are much more likely to rely on algorithms to help guide which purchases we make. For example, when we add a book to our cart on Amazon and then — bam — another suggested book pops up on the screen and we think, “Hey, that one actually looks pretty interesting, too” and we add it to our cart, too, without a second thought.

Recommendation systems with strong algorithms are at the core of today’s most successful online companies such as Amazon, Google, Netflix and Spotify. By endlessly recommending new products that suit their customers’ tastes, these companies provide a personalized, attentive experience across their brand platform, effectively securing customer loyalty.

Check out a YouTube video by NBC News.

How do they work?

Recommender systems can be broken down into two main categories:

• Item-based recommendations
• User-based collaborative filtering