Complete the Look Recommendation System

Building a comprehensive recommendation system prototype for online ecommerce. Please kindly provide proper citation if you plan to use any part of the project.

Introduction

With the accelerated online eCommerce scene driven by the contactless shopping style in recent years, having a great recommendation system is essential to the business' success. However, it has always been challenging to provide any meaningful recommendations with the absence of user interaction history, known as the cold start problem. In this project, we attempted to create a comprehensive recommendation system that recommends both similar and complementary products using the power of deep learning and visual embeddings, which would effectively recommend products without need any knowledge of user preferences, user history, item propensity, or any other data.

Datasets

The dataset used is the shop the look dataset and the complete the look dataset from Pinterest. Thank you for kindly sharing these great data sources to make this project possible.

Quick Run Instructions

Recommend Similar Products

1. Download data - Run cd src/dataset/data and run python download_data.py
2. Get similar product embedding - Run cd src, make sure the in the features/Embedding.py, the class method similar_product_embedding is being selected, then run PYTHONPATH=../:. python features/Embedding.py (be careful this could take up to 2 hours without the presence of a GPU)
3. Recommend Similar Product - Run cd src, make sure the in the recommend.py, the function recommend_similar_products is being selected, then run PYTHONPATH=../:. python recommend.py

Recommend Compatible Products

1. Download data - Run cd src/dataset/data and run python download_data.py
2. Train compatible model - Run cd src and run PYTHONPATH=../:. python models/training.py
3. Get compatible product embedding - Run cd src, make sure the in the features/Embedding.py, the class method similar_product_embedding is being selected, then run PYTHONPATH=../:. python features/Embedding.py (be careful this could take up to 15 hours without the presence of a GPU, 7 hours with GPU)<img
4. Evaluate the compatible model - Run cd src and run PYTHONPATH=../:. python models/evaluate.py
5. Recommend Compatible Product - Run cd src, make sure the in the recommend.py, the function recommend_compatible_products is being selected, then run PYTHONPATH=../:. python recommend.py

Results

Samples of similar product recommendation (on the left is the query product, on the right is the top 5 recommended similar products)

Samples of compatible product recommendation (on the left is the query product, on the right is the top 5 recommended compatible products)