https://github.com/jose-zothner-meyer/facebook-marketplace-recommendation-ranking-system
This project implements a search ranking system for Facebook Marketplace using multi-modal embeddings, transfer learning, and vector search indexing. It aims to improve product ranking & recommendations by leveraging both image & text data from product listings.
https://github.com/jose-zothner-meyer/facebook-marketplace-recommendation-ranking-system
Last synced: about 1 month ago
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This project implements a search ranking system for Facebook Marketplace using multi-modal embeddings, transfer learning, and vector search indexing. It aims to improve product ranking & recommendations by leveraging both image & text data from product listings.
- Host: GitHub
- URL: https://github.com/jose-zothner-meyer/facebook-marketplace-recommendation-ranking-system
- Owner: jose-zothner-meyer
- License: mit
- Created: 2025-01-29T15:53:02.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-02-13T08:30:28.000Z (3 months ago)
- Last Synced: 2025-02-13T09:30:52.714Z (3 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 90.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Facebook Marketplace Recommendation & Ranking System
## Table of Contents
- [Facebook Marketplace Recommendation \& Ranking System](#facebook-marketplace-recommendation--ranking-system)
- [Table of Contents](#table-of-contents)
- [**📌 Project Overview**](#-project-overview)
- [**📌 Project Roadmap**](#-project-roadmap)
- [**📌 1. Data Collection \& Preprocessing**](#-1-data-collection--preprocessing)
- [**📌 2. Feature Extraction**](#-2-feature-extraction)
- [**📌 3. Search Index \& Ranking System**](#-3-search-index--ranking-system)
- [**📌 4. Real-Time Deployment**](#-4-real-time-deployment)
- [**🚀 Technologies \& Tools**](#-technologies--tools)
- [**📝 Next Steps**](#-next-steps)
- [Installation](#installation)
- [**Prerequisites**](#prerequisites)
- [**Step 1: Clone the Repository**](#step-1-clone-the-repository)
- [**Step 2: Create a Virtual Environment**](#step-2-create-a-virtual-environment)
- [**Step 3: Install dependancies**](#step-3-install-dependancies)
---
## **📌 Project Overview**
This project implements a **search ranking system** for **Facebook Marketplace** using **multi-modal embeddings**, **transfer learning**, and **vector search indexing**.
It aims to improve **product ranking & recommendations** by leveraging **both image & text data** from product listings.
---
## **📌 Project Roadmap**
This project involves multiple stages, including **data preprocessing, feature extraction, model training, and ranking optimization**. Below is a structured implementation plan:
### **📌 1. Data Collection & Preprocessing**
✅ **Step 1: Tabular Data Cleaning**
- Standardize **product listings** (e.g., prices, categories, locations).
- Extract **text features** from product descriptions.
- Encode product categories for supervised learning.
✅ **Step 2: Image Preprocessing**
- Resize images to **256x256** pixels.
- Convert all images to **RGB** to ensure uniform input.
- Apply **data augmentation** (if needed) to improve generalization.
✅ **Step 3: Merging Data**
- Link images with their corresponding product descriptions.
- Create a **single dataset** that contains both **images** and **text features**.
---
### **📌 2. Feature Extraction**
✅ **Step 4: Image Embeddings (CNN)**
- Use a **pre-trained ResNet-50** (or EfficientNet) to extract meaningful **image embeddings**.
- Apply **transfer learning** by fine-tuning ResNet-50 on Marketplace categories.
✅ **Step 5: Text Embeddings (CNN or Transformer)**
- Convert text into vector form using:
- **Word2Vec / FastText** for static embeddings.
- **BERT / DistilBERT** for contextual embeddings.
- **CNN** for local feature extraction from text.
✅ **Step 6: Multi-Modal Fusion**
- Concatenate **image embeddings** and **text embeddings**.
- Train a **shallow feedforward network** to classify products based on combined embeddings.
---
### **📌 3. Search Index & Ranking System**
✅ **Step 7: Create the Search Index**
- Store **product embeddings** in a **vector database** (e.g., **FAISS**).
- Use **FAISS Approximate Nearest Neighbors (ANN)** for fast retrieval.
✅ **Step 8: Query Embeddings**
- Convert **user search queries** into an embedding using the trained **text model**.
- Retrieve top **K** nearest product embeddings from the search index.
✅ **Step 9: Final Ranking**
- Apply a **re-ranking model** (Gradient Boosted Trees, LightGBM, or Neural Networks).
- Incorporate **user preferences & personalization** into ranking.
---
### **📌 4. Real-Time Deployment**
✅ **Step 10: Deploy as an API**
- Containerize the **ranking model** using **Docker**.
- Deploy on **AWS Lambda, GCP, or FastAPI** to serve search queries.
- Optimize for **low-latency inference**.
✅ **Step 11: Live Recommendations**
- Store **user interaction history**.
- Apply **collaborative filtering + content-based filtering** for **personalized ranking**.
---
## **🚀 Technologies & Tools**
| **Component** | **Technology Used** |
|---------------------|--------------------|
| **📊 Data Processing** | `pandas`, `numpy`, `scikit-learn` |
| **🖼️ Image Processing** | `OpenCV`, `PIL`, `torchvision` |
| **🧠 Deep Learning** | `PyTorch` (ResNet, CNN, Transformers) |
| **📡 Search Indexing** | `FAISS` (Vector Search), `Annoy` |
| **🖥️ Deployment** | `Docker`, `FastAPI`, `AWS/GCP` |
---
## **📝 Next Steps**
1. **Start by implementing data cleaning & preprocessing** (`main.py`).
2. **Train models for feature extraction (CNN + Transformer)**.
3. **Integrate FAISS for efficient search ranking**.
4. **Deploy the ranking system as a real-time API**.
---
This project is a **real-world application** of **multi-modal AI + search indexing**.
Let me know if you need help with specific components! 🚀🔥
---
## Installation
### **Prerequisites**
Ensure you have **Python 3.13+** and **Conda** or **pip** installed.
### **Step 1: Clone the Repository**
```bash
git clone https://github.com/jose-zothner-meyer/Facebook-marketplace-recommendation-ranking-system.git
cd Facebook-marketplace-recommendation-ranking-system
```
### **Step 2: Create a Virtual Environment**
```bash
conda create --name marketplace_env python=3.13.1
conda activate marketplace_env
```
### **Step 3: Install dependancies**
```bash
pip install -r requirements.txt
```