Movie Recommendation System

Author(s)

Tylah Jenkins (14248037)
Dayhe Kwon (25531308)
Aabhii Taneja (13568821)
Harshit Setia (25206804)

Date: 01/08/2025

Overview & Purpose

This workflow builds a simple movie recommendation system using the MovieLens 100k dataset to predict user preferences and movie ratings.

Data Used

Uses MovieLens 100k dataset files: u.data (for the final working model), uX.base/test files if running the hyperparameter tuning, and u.user/u.item if running the hybrid model.

Required Location: On the user's Desktop is the most convenient location.

Methodology

Workflow steps include:

Data loading (ingestion) and initial inspection
Cleaning & preprocessing (column renaming, cleaning was conducted and trusted from source)
Data splitting (Partitioned - 80/20 train/test split) / Test validation set checks
Spark environment initialisation and required transformations
Model training/prediction (Collaborative Filtering)
Output generation (predicted vs actual)
Evaluation & Analysis

How to run the Workflow

Place data files in the specified location.
Open workflow in KNIME.
Drag mouse over all nodes in the 'Final Working Model' section and select execute all.

Outputs

Actual vs. Predicted ratings.
Table of Top 10 Recommendations table for each user (unrated movies).
Evaluation metrics table: RMSE and Recall.

Evaluation Metrics

System evaluated using RMSD and Recall metrics on the u.data set (training/test split).

Assumptions

Data files are complete, unaltered, and tab-separated.
Input data includes required pre-cleaning (users >= 20 ratings, with complete demographics).
Rating scale is a 1-5.
Data provided is reliable and credible.

	FINAL - Movie Recommendation Model (CF)
	Testing - CF Hyperparameter Tuning (Lambda, Rank, Iteration)
	Testing - Hybrid Model (Collaborative Filtering & Feature Generation - Linear Regression) (1st Iteration)

Movie Recommendation System (ml-100K)