Week 3 Assignment 3A Approach - Global Baseline Estimate

Introduction (Approach)

The goal of this assignment is to implement a Global Baseline Estimate recommender using the movie ratings data I already collected and stored in PostgreSQL for Assignment 2A. The idea behind a Global Baseline Estimate is that you can predict how a person would likely rate a movie they haven’t seen yet, using three pieces of information: the overall average rating across everyone and every movie, how much that specific movie tends to run above or below the average, and how much that specific person tends to rate above or below the average.

My plan is to reuse the same tables from 2A rather than rebuilding anything from scratch. I’ll query the ratings back into R, then calculate the overall mean rating, each movie’s average deviation from that mean, and each user’s average deviation from that mean. Combining those three numbers gives an estimated rating for any user-movie pair, including ones where that person never actually rated that movie.

The main challenge I anticipate is that some of my movies and users have very few ratings. In 2A, Dead Man’s Wire only had 3 ratings total, so any deviation calculated from that movie is based on very little data and might not be reliable. I’ll need to keep that limitation in mind when interpreting the estimates rather than treating every prediction as equally trustworthy. A second challenge will be deciding exactly which user-movie pairs are worth estimating, since with only 12 users and 6 movies there aren’t that many missing combinations to predict in the first place.