Recommendation System

Introduction to Embedding-Based Recommender Systems

Learn to build a simple matrix factorization recommender in TensorFlow

Photo by Johannes Plenio on Unsplash

They are everywhere: these sometimes fantastic, sometimes poor, and sometimes even funny recommendations on major websites like Amazon, Netflix, or Spotify, telling you what to buy, watch or listen to next. While recommender systems are convenient for us users — we get inspired to try new things — the companies especially benefit from them.

To understand to which extent, let us take a look at some numbers from the paper Measuring the Business Value of Recommender Systems by Dietmar Jannach and Michael Jugovac [1]. From their paper:

• Netflix: “75 % of what people watch is from some sort of recommendation” (this one is even from Medium!)
• Youtube: “60 % of the clicks on the home screen are on the recommendations”
• Amazon: “about 35 % of their sales originate from cross-sales (i.e., recommendation)”, where their means Amazon

In this paper [1] you can find more interesting statements about increased CTRs, engagement, and sales that you can get from employing recommender systems.