/Recommendations-with-IBM-Watson-Studio

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Recommendations with IBM Watson Studio

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  Building an Article Recommendation Engine using IBM Watson Studio platform.
project status

The project involves analyses of the interactions that users have with articles on the IBM Watson Studio platform, and make recommendations to them about new articles we think they will like.

Blog

Read detailed step by step blog on the project here.

Directory Structure

.
├── data
│   ├── articles_community.csv - articles available on the IBM platform
│   └── user-item-interactions.csv - list of articles that users interact with
├── recommendation.html - Jupyter notebook in html format
├── README.md - documentation for the project
└── recommendation.ipynb - Jupyter Notebook for project

Tasks

The project is divided into the following tasks:

  1. Exploratory Data Analysis

    Perform exploratory data analysis to understand the recommendation system data.

  2. Rank Based Recommendations

    Recommend the most popular articles based on number of user interactions.

  3. User-User Based Collaborative Filtering

    Recommend articles to users based on similarity with other users' interactions.

  4. Content Based Recommendations (optional)

    Build content-based recommendations using NLP on article text (optional).

  5. Matrix Factorization

    Apply matrix factorization techniques to predict articles users may interact with.

Tools Used

- pandas
- numpy
- matplotlib
- pickle

Instructions

In a terminal or command window, navigate to the current project directory Recommendations-with-IBM/ and run the following command:

jupyter notebook recommendation.ipynb

This will open the Jupyter Notebook in your local browser.

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