“What Should I Watch Next?” — Exploring Movie Recommender Systems, part 1: Popul...
source link: https://www.tuicool.com/articles/emQ3MbZ
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
Recommender systems. What are they, and why should you care?
Well, it turns out, everywhere uses recommender systems these days. The New York Times, Reddit, YouTube, and Amazon (to name a few) all make use of these systems in various ways to drive traffic and sales, and bring you, the user, what you’re looking for.
When people think of movie recommenders, they’ll most frequently think about Netflix, whose algorithm is such to keep users coming back again and again to watch new and exciting things.
I decided to make my own recommender system, so that I could recommend myself new movies to watch. I created four different systems, from simple to more complex: A popularity filter, a content-based recommender, a collaborative recommender using SVD matrix factorization, and a hybrid recommender of both the collaborative and content-based.
In the first post of this series, I’ll be talking about the data process, and exploring how I made my popularity filter.
Recommend
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK