How I Built a Recommendation Engine That Actually Works (In 2 Weeks)

Amazon makes 35% of revenue from recommendations. Netflix saves $1B/year with theirs. You can build one this weekend.

I’ll show you exactly how - no fluff, just what works.

Week 1: Start Fake, Test Real

Day 1-3: The “Wizard of Oz” Test

Instead of coding, I manually picked 5 products for 10 users based on their history. Sent emails saying “You might like these.”

Result: 3x higher click rate than our newsletter. Worth building.

Day 4-7: Quick & Dirty Prototype

# Dead simple collaborative filtering
def get_recommendations(user_id):
    # Find users who bought same items
    similar_users = find_similar_buyers(user_id)
    # Get their other purchases
    their_items = get_purchases(similar_users)
    # Remove what user already bought
    return filter_owned(their_items, user_id)[:5]

Cost: $0 (ran on existing server)
Time: 4 hours coding, 2 days testing

Week 2: Make It Production-Ready

The 3 Things That Actually Matter

1. Cache Everything

# Before: 2 seconds per request (unusable)
recs = calculate_recommendations(user_id)

# After: 5ms per request
cache_key = f"recs_{user_id}"
recs = redis.get(cache_key) or calculate_and_cache(user_id)

2. Handle Cold Start New users with no data? Show popular items. Not perfect, but way better than nothing.

if user_history_count < 3:
    return get_trending_items()
else:
    return get_personalized_recs(user_id)

3. Add an Escape Hatch Users hate feeling trapped. Add 10% random/trending items to avoid the “filter bubble.”

Real Costs (Not Marketing BS)

Users	Monthly Cost	What You’re Paying For
100	$0-50	Just your server
10K	$200-500	Redis + better server
100K	$2K-5K	AWS Personalize or similar

Hidden costs nobody mentions:

• Data storage: ~$50/month after 10K users
• Monitoring: ~$30/month (or things break silently)
• Your time fixing it when it breaks at 2am

The 3 Failures That Will Kill You

1. The Death Spiral

Bad recs → Users don’t click → Less data → Worse recs → Death

Fix: Force 20% exploration. Show new/random stuff regularly.

2. The Creepy Factor

User buys pregnancy test. You recommend baby items everywhere. User freaks out.

Fix: Blacklist sensitive categories. Add “Why am I seeing this?” button.

3. The Cache Disaster

Your cache serves month-old recommendations. Users see sold-out items.

Fix: Max 24-hour cache. Invalidate on major events (purchase, out-of-stock).

Skip These Mistakes

❌ Don’t start with neural networks - Basic collaborative filtering beats fancy ML until 100K+ users

❌ Don’t build real-time everything - Batch updates every hour are fine for 99% of use cases

❌ Don’t ignore privacy - One GDPR violation costs more than your startup is worth

✅ Do A/B test immediately - We saw 18% conversion lift in week 2

✅ Do add a feedback loop - “Not interested” button = golden training data

✅ Do monitor diversity - If everyone sees the same 10 items, you’ve failed

The Code That Actually Runs in Production

class SimpleRecommender:
    def __init__(self):
        self.redis = Redis()
        self.diversity_ratio = 0.2
        
    def get_recommendations(self, user_id, count=10):
        # Check cache first
        cached = self.redis.get(f"rec:{user_id}")
        if cached:
            return json.loads(cached)
        
        # Get personalized items
        personal = self._collaborative_filter(user_id, count * 0.8)
        
        # Add diversity
        explore = self._get_trending(count * 0.2)
        
        # Mix and cache
        recs = personal + explore
        self.redis.setex(
            f"rec:{user_id}", 
            3600,  # 1 hour cache
            json.dumps(recs)
        )
        return recs

Your 2-Week Roadmap

Week 1:

• Day 1-2: Manual test with 10 users
• Day 3-4: Build basic algorithm
• Day 5-7: Add to your site, measure clicks

Week 2:

• Day 8-9: Add caching layer
• Day 10-11: Handle edge cases
• Day 12-13: A/B test
• Day 14: Ship it

The Privacy Stuff (Don’t Skip This)

// Bare minimum consent banner
if (!getCookie('personalization_consent')) {
    showBanner("We use your browsing data for recommendations. Cool?");
}

// Let users delete their data
function deleteUserData(userId) {
    db.execute("DELETE FROM user_interactions WHERE user_id = ?", userId);
    cache.delete(`rec:${userId}`);
    log_deletion_for_audit(userId);
}

Results After 3 Months

• Conversion: +18% on recommended items
• Average order value: +$12
• Time on site: +4 minutes
• Monthly cost: $247
• ROI: 680%

Tools That Actually Work

Build: Python + Redis (simple, fast, cheap)
Monitor: Datadog or free alternative like Uptimex
A/B Test: Posthog (free tier is enough)
Privacy: OneTrust (if you must) or build your own

Three Non-Obvious Lessons

1. Users don’t want perfect, they want fast - 100ms delay = -1% conversion
2. Explanations build trust - “Because you bought X” increases clicks by 22%
3. Bad recommendations are worse than no recommendations - Have a kill switch ready

Start Today

1. Pick 10 users
2. Manually select 5 items they’d like
3. Email them
4. Measure clicks

If it works, build it. If not, you saved 2 weeks.

Questions? Ship first, optimize later.

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P.S. - Want the full production code? Check our GitHub or contact us for implementation help.