Embedding-Based Retrieval for Airbnb Search

Airbnb's search function plays a crucial role in helping guests find the perfect stay, but it's a challenging task due to the large number of available homes and complex search queries. To tackle this, Airbnb built an Embedding-Based Retrieval (EBR) search system to narrow down the initial set of eligible homes into a smaller pool. The EBR system consists of three key components: constructing training data, designing the model architecture, and developing an online serving strategy. The training data pipeline leverages contrastive learning to map homes and search queries into numerical vectors. The model architecture follows a traditional two-tower network design, with one tower processing features about the home listing and the other processing features related to the search query. The listing tower is computed offline daily, reducing online latency. For online serving, Airbnb explored approximate nearest neighbor (ANN) solutions and chose an inverted file index (IVF) due to its better trade-off between speed and performance. The IVF solution clusters listings beforehand and retrieves homes from the top clusters by treating cluster assignments as a standard search filter. The EBR system led to a statistically significant gain in overall bookings when A/B tested, effectively incorporating query context and ranking homes more accurately during retrieval. The system has been fully launched in both Search and Email Marketing production.