Data Science Consultant, Fractal Analytics
Senior Data Platform Engineer, GoodRx
Principal Software Engineer, Afiniti
Senior Applied Scientist, Amazon
In this Graph Based Recommender System Project, you will build a recommender system project for eCommerce platforms and learn to use FAISS for efficient similarity search.
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Business Objective:
In the previous project; (Build a Graph Based Recommendation System in Python), we understood what recommendation systems are and how they work.
We also understood what clickstream data is and how to convert the clickstream data into various types of graphs.
This project will see how to build a recommendation system for our eCommerce platform by converting the generated graphs into embeddings and further using similarity search.
Aim:
To build a Graph based recommender system that will recommend similar products to the users of our e-commerce platform.
Data Overview :
The dataset contains user information over 9 attributes for an eCommerce website
The data has been converted into graph format for further use.
Tech Stack:
Language: Python
Packages: pandas, numpy, pecanpy, gensim, plotly, umap, faiss
File Management: Parquet
Prerequisites:
Build a Graph Based Recommendation System in Python
Understanding of Word2Vec technique
File structure:
Data - The data folder consists of 3 subfolders.
ConstructedGraph - The generated graphs from the previous project are saved here.
Edg_Graphs_DataFile - The graphs are saved in (.edg) format for further processing
Embedding_Data - Generated embeddings are saved here
Documentation - This folder consists of different learning resources and references
Model - Generated models are saved in this folder
Notebook - This folder contains the notebooks we have used
Data Exploration and Data Analysis [RECAP].ipynb
GraphConstruct [RECAP].ipynb
Deepwalk and Node2vec Model Training.ipynb
Result Analysis.ipynb
Embedding Vector Search with ANN FAISS (Recommendation).ipynb
Constants.py
Script - Scripts available in .py file
requirements.txt - The requirements.txt file has all the required libraries with respective versions. Kindly install the requirements at the beginning.
Approach:
Understand the problem statement
Deepwalk and Node2vec Model training
Import the necessary packages and libraries
Read the selected graph
Save the graph in .edg format
Graph random walk generation
Model Building with Word2Vec and Node2Vec
Save the embeddings in parquet format
Result analysis and Visualization
Import the necessary packages and libraries
Read the data
Define a function for generating levels of category
Use UMAP (Uniform Manifold Approximation and Projection) method as a dimension reduction strategy
Visualize the clusters
Embedding vector search
Import the necessary packages and libraries
Use FAISS (Facebook AI Similarity Search) for generating recommendations
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