Airbnb: Listing Embeddings for Similar Listing Recommendations and Real-time Personalization in Search

In this blog post we describe a Listing Embedding technique we developed and deployed at Airbnb for the purpose of improving Similar Listing Recommendations and Real-Time Personalization in Search Ranking. The embeddings are vector representations of Airbnb homes learned from search sessions that allow us to measure similarities between listings. They effectively encode many listing features, such as location, price, listing type, architecture and listing style, all using only 32 float numbers. We believe that the embedding approach for personalization and recommendation is very powerful and useful for any type of online marketplace on the Web.

The approach is almost comically effective (the above pictures are of different locations!). Really interesting work, and detailed writeup.


Want to receive more content like this in your inbox?