Dual Supervision: Smarter Recommendations for Living Spaces
Choosing a place to live isn’t just a transaction — it’s one of the most important decisions people make, and the process is rarely straightforward. Price and square footage matter, but so do lifestyle, convenience, and the little details that make a space feel right. Traditional recommendation systems often miss this complexity because they lean too heavily on one type of signal.
That’s the gap my Dual Supervision Recommendation Engine is designed to close. It combines two complementary forms of learning:
- Explicit feedback — what people say they want: number of bedrooms, amenities, commute time, or budget.
- Implicit feedback — what their actions reveal: clicks, saves, tours, or actual move-in choices.
By supervising on both, the model learns to balance stated intentions with real-world behavior. Someone might insist on “walking distance to downtown,” yet repeatedly explore quieter neighborhoods. Dual supervision helps surface recommendations that respect both the wish list and the lived reality.
The project highlights a broader principle: human decision-making is multi-dimensional, and our models should be too. While I focused on housing, this approach applies anywhere personalization matters — from retail and media to financial products and healthcare.
This project was both a technical exploration and a reflection on how to build tools that better align with human behavior. The result is a recommendation engine that feels less like a generic search filter and more like a trusted guide.