Approach

The Goal of this assignment is to use a Global Baseline Estimate (GBE) recommendation system using R and see how personalization changes results in predictions. With the movie rating data collected in the previous assignment (Assignment_2A_Movie ratings), I aim to predict rating values for movies that reviewers have not yet seen. This is possible with the GBE formula. The dataset has missing values (NA/NULL) and these will be the target for our prediction algorithm. This model will account for reviewers movie bias preferences and popularity towards the item bias.

Dataset contains ratings of 6 reviewers (Liz, Jed, Brenda, Jamie, Justice, Theresa) and 6 horror/thriller movies(The Substance, Nosferatu, Frankenstein, 28 years later, Sinners, The Conjuring 4)

Source: Dataset from local PostgreSQL populated via Assigment2A_movie_rating.sql

Implementation Steps

  1. Connecting/Loading Data

    PostgreSQL database connection with DBI and RPostgres, import raw ratings table into R data frame, and convert NULL 
values in SQL into NA values in R for calculation

  2. Bias Calculation and apply GBE forumla With dyplyr, use Group_by function and calculate:

    • Mean of all rating
    • Group: title –> movies bias
    • Group: reviewer_name –> User bias GBE formula for User+movie pair

  3. Comparison and Data Visualization

    Raw average vs GBE average predictions and seeing the user bias distribution. Find the predicted score for a reviewer, in this case, I will use Liz as an example.