文件列表:

Model/
Notebook/
Script/
requirements.txt

# Graph-Based Recommendation System for an eCommerce Platform ## Overview This project developed a Graph-based recommendation system for an eCommerce platform, leveraging user purchase and search history to recommend products. By transforming user interaction data into graph embeddings and using ANN-FAISS model, the system aimed to enhance product discoverability and user experience. ## Data Overview The dataset comprised user interactions on the eCommerce website, spanning 9 attributes. This was ransformed into a graph format for analysis and modeling. ## Technology Stack - **Programming Language:** Python - **Libraries:** pandas, DuckDB, numpy, pecanpy, gensim, plotly, UMAP, ANN, FAISS - **File Management:** Parquet files for efficient data storage - **Database Management:** SQL for data querying ## Approach ### 1. Problem Understanding Researched the requirements for creating a recommendation system that uses graph theory to suggest products based on historical user data. ### 2. Data Preparation Extracted relevant data from the provided dataset and optimised it for the analysis. - **Libraries Used:** pandas, numpy, DuckDB ### 3. Deepwalk and Node2vec Model Training - **Libraries Used:** Pecanpy, gensim - **Main Steps:** - Performed graph random walks to generate sequences for embeddings. - Trained the Deepwalk and Node2Vec models using the gensim library. ### 4. Result Analysis and Visualization - **Libraries Used:** UMAP, plotly - **Main Steps:** - Categorized products into hierarchical levels for detailed analysis. - Applied UMAP for dimensionality reduction of embeddings to visualize data clusters. - Used Plotly for interactive 2D and 3D visualization of product clusters. ### 5. Embedding Vector Search with FAISS - **Libraries Used:** FAISS - **Main Steps:** - Initialized the FAISS library for efficient similarity search. - Used FAISS to find similar products based on embedding proximity. - Generated product recommendations for users based on similarity scores. ## Folder Structure - **Model:** Stores the trained Deepwalk and Node2Vec models. - **Notebooks:** Contains Jupyter notebooks used in the project: 1. Data Optimisation 2. Data Exploration and Data Analysis 3. Deepwalk and Node2Vec Model Training 4. Graph Construction 5. Result Analysis 6. Embedding Vector Search and Recommendation with FAISS - **Scripts:** Python scripts are available for reproducibility and automation. - **requirements.txt:** Lists all necessary libraries with versions. Install these requirements to ensure compatibility. ## Installation To set up the environment for this project, install the dependencies listed in `requirements.txt` using the following command: ```sh pip install -r requirements.txt ``` ## Usage Navigate through the notebooks in the provided order to understand the workflow. Each notebook is self-contained with comments and instructions for ease of use. ## Acknowledgments - The pecanpy and gensim libraries for providing efficient implementations of node embeddings. - The FAISS library by Facebook AI for enabling high-performance similarity search. - The UMAP library for intuitive dimensionality reduction and data visualization techniques.

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