Created Recommender systems using TMDB movie dataset by leveraging the concepts of Content Based Systems and Collaborative Filtering.
- 1.1 Dropping Features
- 1.2 Updating Features
- 1.3 Feature Engineering
- 2.1 Creating a simple recommender system to show the top N movies at an overall level
- 2.2 Creating a simple recommender system to show the top N movies based on genre
- 3.1 Movie Overview Based Recommender
- 3.2 Movie Metadata Based Recommender
- 4.1 Memory-Based Systems
- 4.1.1 User-Based Collaborative Filtering
- 4.1.2 Item-Based Collaborative Filtering
- 4.2 Model-Based Systems
- pandas - 1.4.4
- numpy - 1.21.5
- SentenceTransformer (from sentence_transformers) - 2.2.2
- Model used: all-mpnet-base-v2
- cosine_similarity (from sklearn.metrics.pairwise) - 1.0.2
- SVDpp (from surprise) - 1.1.3
- Reader, Dataset (from surprise) - 1.1.3
- cross_validate (from surprise.model_selection) - 1.1.3
Top 15 movies at an Overall level
Top 15 movies based on 'Horror' genre
- Top 15 movies recommended to a user who has watched 'The Godfather'
- Top 15 movies recommended to a user who has watched 'The Notebook'
- Top 15 movies recommended to a user who has watched 'The Godfather'
- Top 15 movies recommended to a user who has watched 'The Dark Knight'
- Top 15 movies recommended to a user who has watched the movies in the below movie list
movie_list = ['The Lion King', 'Se7en', 'Toy Story', 'Blade Runner', 'Quantum of Solace', 'Casino Royale', 'Skyfall']
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User-Based Collaborative Filtering
- Top 15 movies recommended for user number 39
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Item-Based Collaborative Filtering
- Top 15 movies recommended for user number 39
- Top 15 movies recommended for user number 39
http://infolab.stanford.edu/~ullman/mmds/ch9.pdf
https://www.kaggle.com/code/rounakbanik/movie-recommender-systems
https://medium.com/grabngoinfo/recommendation-system-user-based-collaborative-filtering-a2e76e3e15c4
https://github.com/NicolasHug/Surprise/tree/master/examples