New Method | Your Recommendation Engine is Lying to You: Machine Learning Matrix Completion & Recommender Systems
- Maria Alice Maia
- Jul 28
- 2 min read
Let's be brutally honest. Most corporate recommendation engines are stuck in kindergarten. They operate on a laughably simple logic: "You bought a hammer, so you must want more hammers. All the hammers. Forever." Best case scenario, they'll offer you nails.
This is “Doing Data Wrong” at its most expensive. I see it constantly in the travel industry. A customer books a quiet, boutique hotel in Santorini. The engine’s brilliant takeaway? "This person likes beaches." For the next six months, it bombards them with ads for crowded, all-inclusive resorts in Cancun. The result? Annoyance, irrelevance, and a massive missed opportunity.
The company didn't sell a vacation; they sold a vibe. The customer wasn't buying "a beach," they were buying "secluded luxury," "architectural beauty," or "a quiet escape." These are latent preferences—powerful, unstated drivers of choice that a simple, rule-based system can't see.
This is where we stop playing games and get serious. Cutting-edge research, like the work of Susan Athey and Guido Imbens, highlights methods like Matrix Completion that were battle-tested in challenges like the Netflix Prize. Think of it not as a dry algorithm, but as a way to see the invisible.
Instead of a giant, sparse matrix of who-bought-what, this method "completes" the picture. It uses the choices of thousands of customers to infer the handful of core “preference dimensions” that truly matter. It uncovers that your Santorini customer isn't just a "beach lover" but a "quiet luxury seeker" who might also be the perfect candidate for a high-end safari lodge in Kenya or a secluded cabin in the Swiss Alps. The machine learns the context of the choice, not just the category.
This is how you stop insulting your customers' intelligence and start anticipating their desires.
For Managers: What to Demand
Stop accepting dashboards that show you simplistic purchase correlations. Ask your tech and data teams: "Are we just matching keywords, or are we building a model that understands the unspoken preferences of our best customers? Are we predicting their next desire or just reacting to their last purchase?"
For Tech Professionals: How to Deliver Real Value
Move beyond basic collaborative filtering. Integrate the structured thinking of economic choice models with the power of machine learning. The goal isn't just to predict a rating, but to build a system that can answer counterfactuals: "How would this customer's choice change if we offered a different package or a different price?" By structuring the problem around latent preferences, you build a far more robust and insightful engine.
I didn’t accumulate knowledge from places like HEC, Berkeley, and FGV, or from the front lines at companies like Itaú and Ambev, to keep it to myself. This knowledge isn't mine to hoard. It’s for us to use, to fix what's broken, and to build value that is real and tangible.
Let's elevate the practice together.
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