Problem Statement and Metrics
Explore how to frame the problem of rental search ranking by predicting bookings using a classification model. Understand the importance of choosing appropriate offline metrics such as normalized discounted cumulative gain (nDCG) and online metrics like conversion rate. Learn techniques to handle imbalanced data and design training, validation, and inference strategies for a practical ranking system.
Airbnb rental search ranking
1. Problem statement
Airbnb users often search for homes in a specific location. The platform must return relevant stays—but more than that, it must return homes users are likely to book. So the goal of the ranking system is simple:
Rank the homes such that those most likely to be booked appear higher in the search results.
A naive method might use keyword matching or hand-crafted scoring—like sorting based on similarity between the query and listing descriptions. But this fails in practice. Text ...