Most data scientists optimize MAP@K, Recall, NDCG, but does that actually improve recommendations?
At Lucklytics, we go beyond metrics, ensuring real engagement & retention. Here’s why we dropped MAP by 3x—and got better results.
🎯 The Problem: When High MAP Fails Users
Optimizing MAP@10 should improve recommendations, right? Not always.
❌ Problem: Models push mainstream hits, ignoring user context.
❌ Result: Recommendations become generic, reducing engagement.
📌 Example: A user watches Aladdin. A high-MAP model suggests:
1️⃣ A war drama
2️⃣ A random action film
3️⃣ A trending blockbuster
Why? Popularity bias—high-frequency items dominate, even if irrelevant.
📌 Step 1: Fighting Popularity Bias
🔍 Most recommender datasets follow power-law distributions—a few top items overwhelm the system.
📊 How to detect it?
✅ Compare model output with a simple popularity-based recommender.
✅ If 50%+ of results match, you have bias.
At Lucklytics, we test for popularity intersection—if 40%+ recommendations match a naïve popularity model, we adjust.
🛠️ Step 2: Optimizing for Serendipity
🔹 Serendipity = Recommending unexpected, yet relevant content.
🔹 MAP-focused models → Generic, overused content.
🔹 Serendipity models → More diverse & engaging suggestions.
📌 But too much serendipity can backfire!
⚠️ Too many obscure items = Users lose interest.
At Lucklytics, we balance:
✅ Recall without Popularity Bias (removes top-100 frequent items)
✅ Mean Inverse User Frequency (MIUF) (favors long-tail content)
✅ Controlled Popularity Intersection (limits trending content dominance)
🔄 Step 3: The Power of Visual Evaluation
🚫 Raw metrics alone won’t save your model. A high-MAP system can still fail in practice.
👀 Solution? Visual Testing!
Instead of relying only on numbers, Lucklytics manually reviews recommendations across diverse user types:
📌 New users (cold start)
📌 Niche users (specific tastes)
📌 General users (broad preferences)
🔬 Pro tip: A/B test against human-selected recommendations—would a film critic suggest the same content?
🏆 Lucklytics’ Balanced Approach to RecSys
After hundreds of tests, we found:
🚫 High MAP, 60%+ Popularity Intersection → Overly generic.
🚫 Too much Serendipity → Recommends niche content no one engages with.
✅ MAP ~0.045, Recall 0.11, Popularity Intersection < 15% → Balanced, personalized recommendations.
🎯 Final takeaway: The best recommender isn’t the one with the highest MAP—it’s the one that keeps users engaged.
🚀 Lucklytics: Turning Data into Growth
At Lucklytics, we go beyond metrics—we build business-driven recommender systems for real engagement.
✅ Reduce popularity bias
✅ Balance MAP, Recall & Serendipity
✅ Use visual evaluation & real-world testing
🔗 Want to improve your recommendation system? Let’s talk: www.lucklytics.com