Movie Recommendation System

Contributors

• Sarah Alqaysi
• Hanmaro Song
• Sean Torres

Objectives

1. ANalyze and Preprocess IMDB data as well as Netflix
2. Use pre-trained Bert model from Hugging Face to analyze the semantics of synopsis for each title
3. Given an input, recommend a list of titles that are similar to the gicen based on genres and plots

How to Use

In the Modeling.ipynb, do the following.

get_recommendation('Fast & Furious', top_k=top_k, use_genre=False)

which will output list of recommendation like

sorted by similarity score in descending order.

if use_genre is set to True, then the model will use both the plot and genre to compute the similarity score.

Data Sources

1. IMDB Movies Analysis
2. IMDb Dataset - From 1888 to 2023
3. Netflix

Preprocessed Data Overview

There are total of 768 semantics columns from HuggingFace Bert model ('bert-base-uncased')

Presentation