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https://github.com/sadegh15khedry/movie-recommendation-system-using-collaborative-filtering

building a movie recommendation system using collaborative filtering techniques.
https://github.com/sadegh15khedry/movie-recommendation-system-using-collaborative-filtering

collaborative-filtering jupyter-notebook matplotlib pandas python recommendation-system scipy seaborn sklearn

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building a movie recommendation system using collaborative filtering techniques.

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README 

        
# Movie Recommendation System using Collaborative Filtering



This repository contains Python code for building a movie recommendation system using collaborative filtering techniques. Below is a breakdown of the files and functionalities included:



1. [Installation](#installation)

2. [Code Description](#code-description)

3. [Author](#author)

4. [License](#license)



## Installation



1. Clone this repository:

   ```bash

   git clone https://github.com/sadegh15khedry/MovieRecommendationSystem.git

   cd Movie-Recommendation-System-Using-Collaborative-Filtering

   ```



2. Install the required libraries using the environment.yml file using conda:

   ```bash

   conda env create -f environment.yml

   ```



3. Download the movieLens datasets (`movies.csv`, `tags.csv`, `ratings.csv`) and update the path to them in the code.



4. Run the `recommendation_system.ipynb` notebook to generate movie recommendations.



## Code Description



### 1. Data Loading and Preprocessing



- Load the datasets (`movies.csv`, `tags.csv`, `ratings.csv`) using pandas.

- Select relevant columns (`tag_df`, `rating_df`, `movie_df`) for further analysis.

- Perform exploratory data analysis (EDA) to understand data shapes, missing values, duplicates, and basic statistics.



### 2. Data Aggregation



- Merge `rating_df` and `movie_df` on `movieId` to create a combined DataFrame (`df`).

- Aggregate ratings to find movies with more than 100 ratings (`agg_df`).

- Merge `df` with `agg_df` to filter out less popular movies (`df_gt100`).



### 3. User-Movie Matrix



- Create a user-movie matrix (`user_movie_matrix`) using `pivot_table`, where rows represent users, columns represent movies, and values represent ratings.



### 4. Normalization and Similarity Calculation



- Normalize `user_movie_matrix` (`matrix_norm`) by subtracting the mean rating of each user.

- Calculate cosine similarities (`user_similarity` and `movie_similarity`) based on `matrix_norm` to find similar users and movies.



### 5. Recommendation Process



- Select a user (`picked_userId`) and set up variables (`number_of_simlar_users`, `user_similarity_threshold`).

- Find similar users (`similar_users`) based on a similarity threshold.

- Identify movies watched by the selected user (`picked_user_watched`) and similar users (`similar_users_movies`).

- Calculate item scores (`item_score`) based on weighted sums of ratings from similar users.

- Sort and print top recommended movies (`ranked_item_score`) based on their scores.



## Results and Outputs



- The script outputs top recommended movies for a selected user (`picked_userId`) based on collaborative filtering.

- Evaluation metrics (e.g., precision, recall) and visualizations (e.g., heatmap of similarity matrices) can be added for performance analysis.



## Further Improvements



- Implement evaluation metrics to quantify the performance of the recommendation system.

- Optimize code efficiency for larger datasets and real-time recommendations.

- Incorporate content-based filtering or hybrid approaches for improved recommendation accuracy.



## Author



- Sadegh Khedry

  

## License



This project is licensed under the Apache-2.0 License - see the LICENSE.md file for details.