How Ecommerce Companies Use Deep Learning for Business Growth

# Simple similarity-based recommendation
import numpy as np

# user-item matrix (rows=users, cols=items)
R = np.array([
    [5, 3, 0, 1],
    [4, 0, 0, 1],
    [1, 1, 0, 5],
    [0, 0, 5, 4],
])

# cosine similarity between users
def cos_sim(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

scores = [cos_sim(R[0], row) for row in R]
print(scores)

Modern ecommerce recommender architecture:
1. Embeddings (users + items)
2. Candidate generation (retrieval)
3. Ranking model (deep neural net)
4. Re-ranking (business rules)
5. Real-time serving (feature store)

# Semantic search with embeddings (concept)
from sentence_transformers import SentenceTransformer
import numpy as np

model = SentenceTransformer('all-MiniLM-L6-v2')

products = ["red running shoes", "blue sneakers", "leather wallet"]
emb = model.encode(products)

query = "sporty footwear"
q = model.encode([query])[0]

scores = emb @ q
print(sorted(zip(scores, products), reverse=True))

Typical search ranking pipeline:
1. Parse query (intent, filters)
2. Retrieve candidates (inverted index)
3. Score with neural ranker
4. Apply business rules (sponsored)
5. Serve results

# High-level fraud model approach (pseudo)
1. Collect transaction + user + device data
2. Build features:
   - order frequency
   - unusual amounts
   - geo mismatch
   - login velocity
3. Train a binary classifier (or autoencoder)
4. Score each order in real time
5. Flag or block high-risk orders

Key fraud features:
- Device fingerprint
- IP + geo distance
- Time since last order
- Order velocity
- Payment method changes
- Email age
- Review text patterns

# High-level demand forecasting approach
1. Historical sales data (daily/weekly)
2. Add features: promotions, holidays, price
3. Train a time-series model
4. Forecast next N days
5. Feed into inventory + replenishment

Forecast features:
- Sales history
- Price changes
- Promotion flags
- Holidays / festivals
- Weather (optional)
- Competitor price (optional)

# Ticket routing (pseudo pipeline)
1. Incoming support ticket text
2. Preprocess (clean text)
3. Classifier predicts category
   - Shipping, Refund, Payment, Product
4. Route to right queue
5. Priority based on sentiment + history

# Sentiment analysis concept
from transformers import pipeline

clf = pipeline("sentiment-analysis")
results = clf([
  "Delivery was fast and the product is great!",
  "The item arrived damaged and support ignored me."
])
print(results)