Problem Statement

Frame a movie recommendation task around what the viewer is likely to watch. Compare watch-history labels with explicit ratings, then state the candidate set, context, and target before choosing a model.

Introduction

Recommendation systems are used by most of the platforms we use daily.

For example:

1. The Amazon homepage recommends personalized products that we might be interested in.

2. The Pinterest feed is full of pins that we might like based on trends and our historical browsing.

3. Netflix shows movie recommendations based on our taste, trending movies, etc.

This chapter uses a Netflix-style movie recommendation case. The ideas also inform other recommendation tasks, although their feedback, catalog, and success criteria may differ.

Problem statement

The interviewer has asked you to display media (movie/show) recommendations for a Netflix user. Your task is to make recommendations in such a manner that the chance of the user watching them is maximized.