E-commerce Applications

Discover how uubed encoding enhances e-commerce platforms through improved product recommendations, search, and personalization.

Overview

E-commerce platforms benefit from uubed in several key areas:

  • Product Recommendations: Find similar products efficiently
  • Search Enhancement: Improve product search with semantic understanding
  • Customer Behavior Analysis: Encode user interaction patterns
  • Inventory Management: Identify similar products for substitution

Implementation Examples

Product Similarity 

from uubed import encode
import numpy as np

class ProductSimilarityEngine:
    def __init__(self):
        self.encoding_method = "shq64"
    
    def encode_product(self, product):
        # Create product embedding from features
        features = self.extract_features(product)
        return encode(features, method=self.encoding_method)
    
    def extract_features(self, product):
        # Combine various product attributes
        features = []
        
        # Category embedding (one-hot or learned)
        features.extend(self.encode_category(product.category))
        
        # Price tier (normalized)
        features.append(self.normalize_price(product.price))
        
        # Brand embedding
        features.extend(self.encode_brand(product.brand))
        
        # Description embedding (using NLP model)
        desc_embedding = self.text_encoder.encode(product.description)
        features.extend(desc_embedding)
        
        return np.array(features, dtype=np.float32)
    
    def find_similar_products(self, product_id, limit=10):
        target_product = self.get_product(product_id)
        target_encoding = self.encode_product(target_product)
        
        similar_products = []
        for product in self.product_catalog:
            if product.id != product_id:
                product_encoding = self.encode_product(product)
                similarity = self.calculate_similarity(
                    target_encoding, 
                    product_encoding
                )
                similar_products.append((product, similarity))
        
        return sorted(similar_products, key=lambda x: x[1], reverse=True)[:limit]

Recommendation System 

class RecommendationEngine:
    def __init__(self):
        self.user_encoder = UserBehaviorEncoder()
        self.product_encoder = ProductEncoder()
    
    def get_recommendations(self, user_id, num_recommendations=5):
        # Encode user preferences
        user_profile = self.build_user_profile(user_id)
        user_encoding = encode(user_profile, method="t8q64", k=16)
        
        # Find products matching user preferences
        recommendations = []
        for product in self.available_products:
            product_encoding = self.product_encoder.encode(product)
            
            # Calculate user-product compatibility
            compatibility = self.calculate_compatibility(
                user_encoding, 
                product_encoding
            )
            
            recommendations.append((product, compatibility))
        
        # Filter and rank recommendations
        top_recommendations = sorted(
            recommendations, 
            key=lambda x: x[1], 
            reverse=True
        )[:num_recommendations]
        
        return [product for product, score in top_recommendations]
    
    def build_user_profile(self, user_id):
        # Aggregate user behavior data
        user_data = self.get_user_data(user_id)
        
        profile_features = []
        
        # Purchase history
        purchase_categories = self.analyze_purchase_categories(user_data.purchases)
        profile_features.extend(purchase_categories)
        
        # Browse behavior
        browse_patterns = self.analyze_browse_patterns(user_data.page_views)
        profile_features.extend(browse_patterns)
        
        # Search queries
        search_preferences = self.analyze_search_queries(user_data.searches)
        profile_features.extend(search_preferences)
        
        return np.array(profile_features, dtype=np.float32)

Search Enhancement 

class EnhancedProductSearch:
    def __init__(self):
        self.text_encoder = SentenceTransformer('all-MiniLM-L6-v2')
        self.product_embeddings = {}
    
    def index_products(self, product_catalog):
        """Pre-encode all products for fast search."""
        for product in product_catalog:
            # Create searchable text from product
            searchable_text = self.create_search_text(product)
            
            # Generate and encode embedding
            embedding = self.text_encoder.encode(searchable_text)
            encoded = encode(embedding, method="eq64")
            
            self.product_embeddings[product.id] = encoded
    
    def search(self, query, limit=20):
        # Encode search query
        query_embedding = self.text_encoder.encode(query)
        query_encoded = encode(query_embedding, method="eq64")
        
        # Find matching products
        matches = []
        for product_id, product_encoding in self.product_embeddings.items():
            similarity = self.calculate_similarity(query_encoded, product_encoding)
            matches.append((product_id, similarity))
        
        # Return top matches
        top_matches = sorted(matches, key=lambda x: x[1], reverse=True)[:limit]
        return [self.get_product(product_id) for product_id, _ in top_matches]
    
    def create_search_text(self, product):
        # Combine relevant product fields for search
        components = [
            product.name,
            product.description,
            product.category,
            product.brand,
            ' '.join(product.tags)
        ]
        return ' '.join(filter(None, components))

Advanced Use Cases

Dynamic Pricing 

class DynamicPricingEngine:
    def recommend_price(self, product_id):
        product = self.get_product(product_id)
        product_encoding = self.encode_product(product)
        
        # Find similar products
        similar_products = self.find_similar_products(product_encoding)
        
        # Analyze pricing of similar products
        price_recommendations = self.analyze_competitor_pricing(similar_products)
        
        return price_recommendations

Inventory Substitution 

class InventoryManager:
    def find_substitutes(self, out_of_stock_product_id):
        """Find suitable product substitutes when items are out of stock."""
        target_product = self.get_product(out_of_stock_product_id)
        target_encoding = self.encode_product(target_product)
        
        # Find products with high similarity
        substitutes = []
        for product in self.available_inventory:
            if product.category == target_product.category:
                product_encoding = self.encode_product(product)
                similarity = self.calculate_similarity(
                    target_encoding, 
                    product_encoding
                )
                
                if similarity > 0.8:  # High similarity threshold
                    substitutes.append((product, similarity))
        
        return sorted(substitutes, key=lambda x: x[1], reverse=True)

Customer Segmentation 

class CustomerSegmentation:
    def segment_customers(self, customer_base):
        """Segment customers based on encoded behavior patterns."""
        customer_encodings = []
        
        for customer in customer_base:
            behavior_profile = self.build_behavior_profile(customer)
            encoding = encode(behavior_profile, method="mq64")
            customer_encodings.append((customer.id, encoding))
        
        # Cluster customers based on encoded profiles
        segments = self.cluster_encodings(customer_encodings)
        
        return segments
    
    def build_behavior_profile(self, customer):
        # Create behavioral feature vector
        features = []
        
        # Purchase frequency
        features.append(customer.purchase_frequency)
        
        # Average order value
        features.append(self.normalize_value(customer.avg_order_value))
        
        # Category preferences
        features.extend(customer.category_preferences)
        
        # Seasonal patterns
        features.extend(customer.seasonal_patterns)
        
        return np.array(features, dtype=np.float32)

Integration Patterns

Real-time Recommendations 

# Real-time recommendation API
from flask import Flask, jsonify, request
from uubed import encode

app = Flask(__name__)

@app.route('/recommendations/<user_id>')
def get_recommendations(user_id):
    # Get user context
    user_context = request.args.get('context', 'browse')
    current_product = request.args.get('product_id')
    
    # Generate recommendations
    recommendations = recommendation_engine.get_recommendations(
        user_id=user_id,
        context=user_context,
        current_product=current_product
    )
    
    return jsonify({
        'user_id': user_id,
        'recommendations': recommendations,
        'context': user_context
    })

Batch Processing 

# Nightly batch processing for product encoding
from uubed.streaming import batch_encode

def update_product_encodings():
    """Update product encodings in batch for efficiency."""
    products = get_all_products()
    
    # Extract features for all products
    product_features = [extract_features(product) for product in products]
    
    # Batch encode all products
    encoded_features = batch_encode(
        product_features,
        method="shq64",
        batch_size=1000
    )
    
    # Update database
    for product, encoding in zip(products, encoded_features):
        update_product_encoding(product.id, encoding)

Performance Optimization

Caching Strategy 

from redis import Redis
import json

class EncodingCache:
    def __init__(self):
        self.redis = Redis(host='localhost', port=6379, db=0)
        self.ttl = 3600  # 1 hour TTL
    
    def get_product_encoding(self, product_id):
        cache_key = f"product_encoding:{product_id}"
        cached = self.redis.get(cache_key)
        
        if cached:
            return cached.decode('utf-8')
        
        # Generate encoding and cache it
        product = get_product(product_id)
        encoding = self.encode_product(product)
        
        self.redis.setex(cache_key, self.ttl, encoding)
        return encoding

Metrics and Analytics

A/B Testing 

class RecommendationABTest:
    def __init__(self):
        self.control_method = "shq64"
        self.test_method = "t8q64"
    
    def get_recommendations_with_test(self, user_id):
        # Determine test group
        test_group = self.get_test_group(user_id)
        
        encoding_method = (
            self.test_method if test_group == 'B' 
            else self.control_method
        )
        
        recommendations = self.generate_recommendations(
            user_id, 
            encoding_method
        )
        
        # Log for analysis
        self.log_recommendation_event(
            user_id, 
            test_group, 
            recommendations,
            encoding_method
        )
        
        return recommendations

Best Practices

  1. Feature Engineering: Carefully design product and user feature representations
  2. Method Selection: Choose appropriate encoding methods for different use cases
  3. Caching: Cache frequently accessed encodings to improve response times
  4. Batch Processing: Update encodings in batches during off-peak hours
  5. A/B Testing: Test different encoding methods to optimize performance
  6. Monitoring: Track recommendation quality and system performance

Deployment Checklist

  • Set up product feature extraction pipeline
  • Implement user behavior encoding
  • Create recommendation API endpoints
  • Set up caching infrastructure
  • Implement batch processing jobs
  • Add monitoring and analytics
  • Test recommendation quality
  • Deploy A/B testing framework

Copyright © 2024 UUBED Project. Distributed under the MIT License.