Recommendation Engine for a Shop

This README provides instructions for building a recommendation engine using Python, modern technologies, and best practices. The engine integrates with a React-based frontend in the Martix app ecosystem, which includes:

• Order Processing: Built on Go
• Product Catalog: Built on Nest.js
• Admin Panel: Built on .NET

1. Project Overview

• Goal: Suggest products based on user interactions (purchases, clicks, ratings).
• Key Outcomes:
  • Increase sales through personalized recommendations.
  • Improve user engagement and retention.
  • Optimize inventory management by surfacing relevant products.

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2. Prerequisites & Environment Setup

2.1. Technologies & Packages

1. Python 3.x
   Ensure Python 3 is installed.

2. Data Manipulation & Analysis

   • Pandas for handling tabular data.
   • NumPy for numerical computations.

3. Modeling & Machine Learning

   • PyTorch for building and training neural network models.
   • scikit-learn for data splitting, metrics, and preprocessing.

4. Optional Libraries

   • LightFM for hybrid matrix factorization.
   • Surprise for collaborative filtering algorithms.

5. Database & Caching

   • PostgreSQL or MongoDB for data storage (if needed).
   • Redis for caching frequently accessed recommendations.

6. Deployment Tools

   • FastAPI or Flask for building an API.
   • Docker to containerize your application for production.

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Features

• Personalized product recommendations.
• Content-based and collaborative filtering hybrid approach.
• Real-time API to serve recommendations to the frontend.

How It Works

Recommendation Logic

The engine operates on:

1. Collaborative Filtering:

   • Based on user-item interactions (e.g., purchases, ratings).
   • Identifies patterns in user behavior and similarities between users or products.

2. Content-Based Recommendations:

   • Analyzes product attributes like categories, descriptions, or tags.
   • Matches user preferences with product features.

3. Hybrid Approach: Combines collaborative filtering and content-based methods for better accuracy.

Data Flow

1. User Interactions: From the Order Processing Module (Go)

   • Purchase history, product views, and ratings.

2. Product Metadata: From the Product Catalog Module (Nest.js)

   • Product descriptions, categories, and tags.

3. Administrative Input: From the Admin Panel (ASP.NET)

   • Manage feature weights, fine-tune algorithms, and retrain models.

Required Data

From Modules

• Order Processing:

  • User activity logs: user_id, product_id, timestamp, action_type (view, purchase, etc.).

• Product Catalog:

  • Product details: product_id, name, category, tags, price, etc.

• Admin Panel:

  • Settings for model tuning: parameter_name, value.

Preprocessed Data for Training

• User-item interaction matrix (e.g., ratings or purchase frequencies).
• Product feature vectors (e.g., categories, tags).

Synchronization

Data Sync Between Modules

• Kafka:

  • Real-time streaming of user interactions from Order Processing to the Recommendation Engine.
  • Synchronize product catalog updates from Nest.js.

• Database:

  • Shared PostgreSQL or MongoDB for consistent access to interaction and product data.

Example Kafka Topics

• user-interactions: Logs user actions like purchases or views.
• product-updates: Updates product catalog details.
• admin-commands: Admin-triggered tasks like retraining models.

Recommendation Generation

1. Training:

   • Train on historical data for collaborative filtering and feature extraction.
   • Fine-tune parameters with feedback from the admin module.

2. Serving Recommendations:

   • For a given user_id, the engine:
     • Retrieves similar users or products.
     • Scores and ranks products based on relevance.
     • Returns top N recommendations.

3. Real-Time API:

   • The FastAPI server processes requests and fetches recommendations dynamically.

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Required components:

Core packages:

• TensorFlow/Keras for neural network implementation
• Pandas for data manipulation
• NumPy for numerical operations
• Scikit-learn for preprocessing

Data requirements:

• User interaction data (purchases, ratings, clicks)
• Product metadata
• User features (optional)

Additional technologies to consider:

• PostgreSQL/MongoDB for data storage
• Redis for caching recommendations
• FastAPI/Flask for API deployment
• Docker for containerization

Alternative approaches:

• Collaborative filtering using Surprise library
• Content-based using scikit-learn
• Matrix factorization using LightFM
• Deep learning using PyTorch

Technologies and Tools

Backend (Recommendation Engine)

• Programming Language: Python
• Frameworks/Libraries:
  • Data Handling: pandas, numpy
  • Machine Learning: scikit-learn, surprise, tensorflow
  • Model Deployment: FastAPI
  • Data Storage: PostgreSQL (or MongoDB for NoSQL)
  • Message Queue: Apache Kafka (for communication with other modules)

Frontend (React Integration)

• Use Axios or Fetch API for communication with the backend.
• Display recommendations in the Product Detail or Home Page components.

Architecture Overview

+-------------------+      +------------------+      +-------------------+      +------------------+
| Recommendation   |<---->| Product Catalog  |<---->| Order Processing  |<---->| Admin Panel      |
| Engine (Python)  |      | (Nest.js)        |      | (Go)              |      | (.NET)           |
+-------------------+      +------------------+      +-------------------+      +------------------+
         |                              Frontend (React-based mARTIX App)
         +----------------------------------------------------------------+

Setting Up the Recommendation Engine

1. Install Dependencies

# Create a virtual environment
python -m venv recommendation-engine
source recommendation-engine/bin/activate  # On Windows: recommendation-engine\Scripts\activate

# Install required packages
pip install pandas numpy scikit-learn surprise fastapi uvicorn tensorflow psycopg2 kafka-python

2. Data Preparation

• Input Data: Collect user interactions (e.g., purchases, ratings) and product details (e.g., categories, descriptions).
• Preprocessing:
  • Convert raw data into a user-item interaction matrix.
  • Normalize ratings.

Example Code:

import pandas as pd
from sklearn.model_selection import train_test_split

# Load data
interactions = pd.read_csv('user_interactions.csv')
products = pd.read_csv('products.csv')

# Split into training and testing sets
train_data, test_data = train_test_split(interactions, test_size=0.2, random_state=42)

3. Training the Model

Use collaborative filtering and content-based features.

from surprise import SVD, Dataset, Reader
from surprise.model_selection import cross_validate

# Prepare data for Surprise library
reader = Reader(rating_scale=(1, 5))
data = Dataset.load_from_df(train_data[['user_id', 'product_id', 'rating']], reader)

# Train an SVD model
model = SVD()
cross_validate(model, data, cv=5, verbose=True)

# Train on the full dataset
trainset = data.build_full_trainset()
model.fit(trainset)

# Save the model
import pickle
with open('recommendation_model.pkl', 'wb') as f:
    pickle.dump(model, f)

4. API for Recommendations

Expose a FastAPI endpoint to serve recommendations.

from fastapi import FastAPI
import pickle
import pandas as pd

app = FastAPI()

# Load model
with open('recommendation_model.pkl', 'rb') as f:
    model = pickle.load(f)

@app.get("/recommend/{user_id}")
def recommend(user_id: int):
    # Generate recommendations
    predictions = [
        (iid, model.predict(user_id, iid).est)
        for iid in products['product_id'].unique()
    ]
    recommendations = sorted(predictions, key=lambda x: x[1], reverse=True)[:10]
    return {"user_id": user_id, "recommendations": recommendations}

# Run the server
# uvicorn main:app --reload

5. Communication with Frontend

1. Use Axios in React to fetch recommendations:

import axios from 'axios';

const fetchRecommendations = async (userId) => {
  try {
    const response = await axios.get(`/api/recommend/${userId}`);
    return response.data.recommendations;
  } catch (error) {
    console.error("Error fetching recommendations", error);
  }
};

2. Display recommendations in a component:

import React, { useEffect, useState } from 'react';

const Recommendations = ({ userId }) => {
  const [recommendations, setRecommendations] = useState([]);

  useEffect(() => {
    const getRecommendations = async () => {
      const data = await fetchRecommendations(userId);
      setRecommendations(data);
    };
    getRecommendations();
  }, [userId]);

  return (
    <div>
      <h3>Recommended Products</h3>
      <ul>
        {recommendations.map((rec) => (
          <li key={rec[0]}>{`Product ID: ${rec[0]}, Score: ${rec[1]}`}</li>
        ))}
      </ul>
    </div>
  );
};

export default Recommendations;

6. Integration with mARTIX Modules

• Product Catalog (Nest.js): Provide metadata for product features and categories.
• Order Processing (Go): Send user purchase data to the recommendation engine via Kafka.
• Admin Panel (.NET): Allow admins to fine-tune model parameters or retrain models.

7. Deployment

• Backend: Deploy the FastAPI server on Docker or Kubernetes.
• Frontend: Integrate the React component into the mARTIX app.
• Inter-Module Communication: Use Kafka topics for message passing between modules.

# Example Docker Compose for the Recommendation Engine
version: '3.8'
services:
  recommendation:
    build: .
    ports:
      - "8000:8000"
    environment:
      - DATABASE_URL=postgresql://user:password@db/recommendations
  db:
    image: postgres
    environment:
      POSTGRES_USER: user
      POSTGRES_PASSWORD: password

Summary

This recommendation engine uses modern technologies like FastAPI, collaborative filtering (Surprise library), and integrates seamlessly with mARTIX modules via REST and Kafka. It ensures real-time, personalized product recommendations, enhancing the shopping experience for users.