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# Recommendation System Dashboard | ||
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**Explain that this application uses movielns dataset to perform group recommendations** | ||
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## How to run this application | ||
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1. Install all application requirements using pip | ||
```python | ||
pip install -r requirements.txt | ||
``` | ||
2. Get into the src folder and run the app using python | ||
```bash | ||
cd src | ||
python3 app.py | ||
``` | ||
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After starting the app, a web dashboard should be available at [http://127.0.0.1:8050/](http://127.0.0.1:8050/). That web page shows basic data insights on the root page and if you go to the url [http://127.0.0.1:8050/recommendations](http://127.0.0.1:8050/recommendations), you can train a recommendation model selecting his hyper-parameters. | ||
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Once the model is trained, you can select a group of users from the the table that is located under the model hyper-parameters. Another table will show selected users performed ratings. Once you are done creating a group, you can click on the button `GET RECOMMENDATIONS FOR USERS` to generate group recommendations, which will be loaded into another table at the end of the page | ||
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## Data Used | ||
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Data used in this application is located under the folder `data`, that contains the following files: | ||
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- `movies.csv`: contains movie id, title and genre for all available movies | ||
- `ratings.csv`: contains the user id, timestamp and rating performed by each user to a movie | ||
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## Matrix Factorization Algorithm | ||
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Matrix factorization is performed using a self made implementation of the SVD++ algorithm, wich is used to perform matrix factorization on the ratings matrix. | ||
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## How group recommendations are performed | ||
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Group recommendation are obtained `Least Misery Criteria` over the individual recommendation of each user inside a group |
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