Personalized Recommendations Are Here — Powered by Smart Analytics

At Simplico, we’re constantly improving the shopping experience for both customers and business owners. Today, we’re excited to introduce a powerful new feature to our eCommerce system:

🎯 Built-in Product Recommendations Based on Real User Behavior

No third-party plugins. No extra setup. Just smarter selling, right out of the box.

🔍 How It Works

Every time someone visits your store, our system tracks their session (anonymously) and records which product pages they view. Over time, this creates a rich dataset of user behavior — without requiring login or cookies.

Then, using machine learning techniques like collaborative filtering, our platform answers one powerful question:

“What other products do people usually view after this one?”

📊 System Diagram

graph TD
    A["Visitor arrives on site"] --> B["UserLogMiddleware records session + URL"]
    B --> C["Product view extracted from URL"]
    C --> D["Session → Product matrix stored"]
    D --> E["AI Model (implicit ALS) trains on data"]
    E --> F["System predicts related products"]
    F --> G["Recommendations shown on product page"]

🧱 Sample Code: How We Log Product Views

# logs/middleware.py

class UserLogMiddleware:
    def __call__(self, request):
        path = request.path
        if "/product/" in path and request.session.session_key:
            from .models import UserLog
            UserLog.objects.create(
                session_key=request.session.session_key,
                url=path
            )
        return self.get_response(request)

🔍 Sample: Extracting Product ID from URL

import re

def extract_product_id(url):
    match = re.search(r"/product/(\d+)/", url)
    return match.group(1) if match else None

🧠 Sample: Building User-Item Matrix for Recommendations

from scipy.sparse import coo_matrix
from collections import defaultdict

# Example view data from logs
logs = UserLog.objects.all()
data = []
session_map, product_map = {}, {}
session_id, product_id = 0, 0

for log in logs:
    session = log.session_key
    product = extract_product_id(log.url)
    if not session or not product:
        continue
    if session not in session_map:
        session_map[session] = session_id
        session_id += 1
    if product not in product_map:
        product_map[product] = product_id
        product_id += 1
    data.append((session_map[session], product_map[product]))

# Build sparse matrix
rows, cols = zip(*data)
matrix = coo_matrix((len(data) * [1], (rows, cols)))

🤖 Sample: Training the Recommendation Model

from implicit.als import AlternatingLeastSquares

model = AlternatingLeastSquares(factors=50, iterations=15)
model.fit(matrix)

🎯 Sample: Get Similar Products for a Product ID

def recommend_similar(product_id, top_n=5):
    index = product_map.get(str(product_id))
    if index is None:
        return []
    similar_items = model.similar_items(index, N=top_n + 1)
    return [pid for pid_idx, _ in similar_items if (pid := get_product_by_index(pid_idx)) != product_id]

def get_product_by_index(index):
    # reverse map from index → product_id
    for pid, idx in product_map.items():
        if idx == index:
            return pid
    return None

🛍️ Example: "Customers Who Viewed This Also Viewed…"

When a user visits the iPhone Case page, the system might recommend:

Wireless Charger
Tempered Glass Screen Protector
Phone Stand
iPad Mini Case

These aren’t random — they’re based on thousands of real customer sessions.

⚙️ Built-In and Fully Integrated

✅ No configuration needed
✅ Learns from actual product views
✅ Supports both authenticated and guest users
✅ Automatically improves as more data is collected
✅ Secure and fast (recommendations can be cached)

🚀 Ready to Grow Smarter?

If you’re already on our platform, your product pages may already be showing smarter recommendations.
If you’re not yet using Simplico eCommerce — contact us today and let’s upgrade your storefront together.