SIFT: Search Intent-to-Filter Transformer for Multi-Task Personalized Filter Ranking at Airbnb
SIFT replaces manual feature engineering with a transformer-based model that learns guest preferences directly from raw search behavior sequences. It uses a unified representation to simultaneously predict booking likelihood, amenity engagement, and numeric capacity thresholds. This approach improves personalization and filter usage across various tasks while maintaining system extensibility.