https://github.com/herrfeder/udacity-project-recommendations-with-ibm-webapp

https://github.com/herrfeder/udacity-project-recommendations-with-ibm-webapp

Last synced: 27 days ago
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• Host: GitHub
• URL: https://github.com/herrfeder/udacity-project-recommendations-with-ibm-webapp
• Owner: herrfeder
• Created: 2020-03-14T11:56:48.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-03-15T12:20:01.000Z (almost 5 years ago)
• Last Synced: 2024-10-15T19:13:22.759Z (2 months ago)
• Language: Python
• Size: 1.4 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Recommendations With IBM Webapp

This repository is a part of https://github.com/herrfeder/DataScientist/tree/master/Project_04_Recommendations_With_IBM.

It serves as a submodule for modular hosting of the webapp for this project.

## Screenshots

| Control Panel and Recommendation Area | Wordcloud and Most Similiar Users |

|--------------------------------------|--------------------------------------|

| ![](screenshots/recommendations_webapp_top.png) | ![](screenshots/recommendations_webapp_bottom.png) |

## Purpose

This webapp should provide a usable use-case for an recommendation engine. Therefore it combines multiple options of recommendation:

  - Singular Value Decomposition (SVD) based recommendation of similiar users.

  - Content Based recommendation based on an NLP Pipeline for similiar articles.

  - Simple Popularity based recommendation for new users.

  

It will show to the user:

  - the choosen number of recommendations for specific user or article

  - the most often occured words of the title in a wordcloud

  - the most similiar users

  

## Included Files

  

  * __Dockerfile__: Files for setting up Docker container

  * __app/data/user_item_matrix.p__: 2-dimensional DataFrame with Articles in columns and user id's as index

  * __app/data/user-item-interactions.csv__: Source data with all user interactions for all articles

  * __app/app.py__: Holds the layout and the interactivity for the Dash webapp

  * __app/plotlywordcloud.py__: Creates wordcloud, thankfully adapted from https://github.com/PrashantSaikia/Wordcloud-in-Plotly

  * __app/recommendengine.py__: Holds the classes for prepare, analyze and model the recommendation engines

  * __app/static/__: Example images from wikipedia and bootstrap css

## Webapp

An example of this web app is temporary accessible on https://federland.dnshome.de/recommendations .

Please be gentle, the resources are restricted. This app __isn't responsive__.

### Docker Instructions

1. Create directory and download Dockerfile:

    ```

    mkdir recommendation_docker && cd recommendation_docker

    wget https://raw.githubusercontent.com/herrfeder/Udacity-Project-Recommendations-With-IBM-Webapp/148b657e249c2055d48c30b28799de2e4f936e18/Dockerfile

    ```

2. Build Docker Container:

    ```

    docker build . -t recommendation_app

    ```

    As the model will be trained during Build, this will take a while.

    

3. Run Docker Container:

    ```

    docker run -p 8050:8050 recommendation_app:latest

    ```

4. Go to http://127.0.0.1:8050/