Movielens Imdb Exploration

Abstract

1. Introduction

Using the wealth of data available on user preferences, researchers and online streaming companies have extensively researched and deployed movie recommender systems. Most recommendation algorithms process the preferences of each user, and potentially those of other similar users, to predict other movies they might like. However, watching movies is often a social activity shared by multiple actors, such as a group of friends. The social dimension of watching movies contradicts common approaches that strive to satisfy one user at a time. In this project, we propose a movie recommender system that aims to maximize the collective satisfaction of a group of users.

In order to implement our movie recommender system, we use the MovieLens dataset. The data contains 100K ratings from 1K users on 1.7K movies and has been used traditionally for recommender system research. Based on the data, we construct two graphs as follows: i) a user graph that models the similarity of users based on their personal details (age, gender, occupation and location), and ii) a movie graph that models the similarity of movies based on their characteristics (release date, genre and title). Nodes have a vector signal that corresponds to the respective ratings. We plan to enrich the movie graph with features extracted from the IMDb dataset (Internet Movie Database) which contains additional information such as the cast of the movie, production companies and relationships between movies. The graph structure is used to generalize the partial information provided by the ratings.

As mentioned, the main goal of our project is to build a recommendation system using graph neural networks. Moreover, the dataset will enable us to have some insights about movie tastes in different countries and among different age groups. Using various dimensionality reduction and clustering algorithms, we may reveal any polarization in the graph.

2. Objective

Data exploration of MovieLens and IMDb datasets to create Movinder. Movie recommendation system for a group of people.

This project demonstrates how HTML can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• A modern web browser
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Movinder/movielens-imdb-exploration.git
cd movielens-imdb-exploration

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by Movinder and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.