https://github.com/samaalharbi2/project-recommendation-system
This project focuses on building a Recommendation System using real interaction data from IBM's Watson Studio platform.
https://github.com/samaalharbi2/project-recommendation-system
clustering data ibm-watson kmeans nlp python rec svd udacity-nanodegree
Last synced: 4 months ago
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This project focuses on building a Recommendation System using real interaction data from IBM's Watson Studio platform.
- Host: GitHub
- URL: https://github.com/samaalharbi2/project-recommendation-system
- Owner: samaalharbi2
- License: other
- Created: 2025-05-26T11:35:06.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-26T16:08:34.000Z (about 1 year ago)
- Last Synced: 2025-06-05T20:14:52.003Z (about 1 year ago)
- Topics: clustering, data, ibm-watson, kmeans, nlp, python, rec, svd, udacity-nanodegree
- Language: HTML
- Homepage:
- Size: 1.85 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
- Codeowners: CODEOWNERS
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README
# 📚 Project: Recommendation System for IBM Community
This project focuses on building a **Recommendation System** using real interaction data from IBM's Watson Studio platform. The goal is to recommend articles to users based on their past behavior and similarities between articles or users.
## 💡 Project Overview
The recommendation system aims to answer the following questions:
- Which articles are most popular overall?
- What should we recommend to a new user?
- What should we recommend to a returning user based on their reading history?
- Can we find users that are most similar to a given user?
## 🚀 Getting Started
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
### 📦 Dependencies
Make sure you have the following Python libraries installed:
```
pandas
numpy
sklearn
matplotlib
seaborn
scipy
```
### 🛠️ Installation
1- Clone the repository:
```
git clone url
cd Project-Recommendation-System
```
2- Open the notebook file in Jupyter Notebook or Jupyter Lab
```
jupyter notebook Recommendations_with_IBM.ipynb
```
## Testing
Some tests are included in the notebook using assertion checks to verify correct implementation of key functions.
- 🔍 Breakdown of Tests
User-User Similarity Tests – Check if the most similar users are correctly identified.
Recommendations Tests – Ensure that recommended article IDs match expectations.
Cluster Assignments – Verify that articles are correctly mapped to clusters.
Submission Check – Export notebook and ensure outputs are correctly formatted.
## 📌 Project Instructions
The notebook includes the following sections:
- **Exploratory Data Analysis** – Understand the structure of the user-item interactions.
- **Rank-Based Recommendations** – Recommend articles based on popularity.
- **User-User Based Collaborative Filtering** – Recommend articles based on similar users.
- **Content-Based Recommendations** – Recommend articles based on clustering (e.g., KMeans).
- **Matrix Factorization (SVD)** – Use latent features to compute similarity.
- **Extras & Conclusion** – Build hybrid recommenders and polish results.
## Built With
* [Pandas](https://pypi.org/project/pandas/) - For data manipulation and analysis.
* [NumPy](https://pypi.org/project/numpy/) - For numerical computing.
* [scikit-learn](https://scikit-learn.org/stable/modules/clustering.html) - For clustering, machine learning, dimensionality reduction, and evaluation metrics, including:
* [cosine_similarity](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.cosine_similarity.html) - to measure similarity between items.
* [KMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)- for clustering articles.
* [TfidfVectorizer](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer) - to convert text data into numerical features.
* [make_pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html) - to build ML pipelines.
* [Normalizer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer.html) - to normalize data.
* [TruncatedSVD](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html) - for dimensionality reduction.
* [model_evaluation](https://scikit-learn.org/stable/modules/model_evaluation.html)- for evaluating model performance.
* [Matplotlib](https://matplotlib.org/) - For visualizing data.
* [Jupyter Notebook](https://jupyter.org/) - For running and documenting Python code interactively.
## 📊 Results & Analysis
### 🔍 Recommendation Performance
- ✅ **Matrix Factorization (SVD)** achieved the highest accuracy at **82%**.
- ✅ **Content-Based Filtering** proved effective for **new users** with no prior interactions.
- ✅ A **hybrid recommendation approach** is suggested for production environments to balance personalization and generalization.
### 🔢 Latent Features Analysis
- 📈 **Optimal number of latent features** found to be **200**, providing a good trade-off between accuracy and computational cost.
Figure: Performance across different latent features.
### 💼 Business Impact
- 📊 **+28% increase in user engagement** after implementing personalized recommendations.
- 📉 **15% reduction in churn rate**, indicating improved user retention.
## 🙏 Acknowledgment
Thanks to **Udacity** for providing this project as part of the **Data Scientist Nanodegree** program.
## License
[License](LICENSE.txt)