This project is collaborated by Chu-Yun Hsiao, Chia-Yen Ho, Shu-Yun Liu and Yu-Chun Peng.
As movie streaming platforms being prosperous for the past decade, the recommendation engine behind each platform has become even more important in order to retain users by providing more precise movie recommendations. The recommendation engine can be broadly applied in various aspects of our everyday life, such as media, E-commerce, retail etc.
As passionate data scientists who love watching movies in our leisure time, we decided to explore how these streaming platforms recommend movies based on our movie preferences. We created four movie recommendation systems, including demographic filtering, content-based filtering, collaborative filtering, and hybrid engine. Each of which provides movie recommendations based on user preferences or movie features.
The visualization part of the project is done using Tableau dashboard. The access links are as below: https://public.tableau.com/app/profile/chiayenho/viz/Movies_16445197972360/Dashboard1
The original data source is from Kaggle. Link as below: https://www.kaggle.com/tmdb/tmdb-movie-metadata https://www.kaggle.com/rounakbanik/the-movies-dataset
This project is inspired by https://www.kaggle.com/ibtesama/getting-started-with-a-movie-recommendation-system