week_3A_global_baseline_estimate
Introduction/Approach
The goal of this week 3A assignment is to implement a Global Baseline Estimate recommender system in RStudio. The data to be used for this task will come from the professor-provided Movie Ratings Excel file. I selected this option because it contains a larger number of respondents (more than the five in my own dataset), which should provide more observations for testing the recommender system’s output.
In examining the types of recommender systems, and compared to more personalized recommender systems (such as collaborative filtering), the Global Baseline approach facilitates predictions via three components:
an overall average rating (the global mean),
a movie specific bias term, and
a user-specific bias term.
Subsequently, the prediction formula for a specific user’s rating of a particular movie will be that of:
predicted rating = μ + bᵢ + bᵤ
Where the predicted rating is equal to the global mean (μ), plus the movie bias (bᵢ) and the user bias (bᵤ).
Data Preparation
As mentioned prior, the dataset will be taken from the Movie Ratings Excel file provided by Professor Catlin. This data is currently within a wide format, where rows represent individual users and columns represent movies. The cells contain the ratings, and some blanks represent movies that were not viewed (or at least not rated).
Therefore, in my data preparation workflow, after importing the Excel file into R (using the readxl functionality), the dataset will be reshaped into a long format (likely using pivot_longer()), and missing entries will be handled as NA values. This is important to ensure that only observed ratings are used when computing the averages and bias terms.
The resulting dataframe will include the variables user, movie, and rating, similar to how my own dataset was structured in the previous Week 2A assignment.
Computing the Global Baseline Components
The next stage will involve calculating:
The global mean (μ): the average across all observed movie ratings.
The movie bias (bᵢ): for each movie, determine its average rating and subtract the global mean from this figure to discern whether it tends to be rated above or below average.
The user bias (bᵤ): For each user, determine their average rating and subtract the global mean from this figure to discern whether the user tends to rate more stringently or generously than average.
Once these values are computed for all users and movies, it becomes possible to estimate ratings for movies that a reviewer has not rated. Recommendations can then be made by ranking unseen movies by their predicted ratings and selecting the highest.
Anticipated Challenges/Data Issues
Ensuring missing ratings are properly handled and excluded from mean/bias calculations.
Confirming that the RStudio implementations produces results that align with the spreadsheet logic and algorithm.
Verifying the garnered predictions against one or more examples executed within the confines of the spreadsheet.
Optional Endeavor
Once the Global Baseline Estimate recommender system in established, a small function may be developed that takes arguments for a specific user and a specific movie. The function would then use the ratings dataframe and the precomputed baseline components to output the predicted rating for that user-movie pair.