The guest journey, updated in real time: extending Airbnb’s sequence recommender with Chronon

How two new Chronon capabilities, Push Mode and NRT Model Transform, allows us to provide more relevant search results instantly as a guest explores, rather than waiting for the next batch run.

By: Pengyu Hou, Yuli Han, Daochen Zha, Haozhen Ding, Xin Liu, Sophie Wang, Pallavi Adusumilli, Sherry Li, Henry Saputra, Chun How Tan, Huiji Gao, Yan Zhang, Stephanie Moyerman, Yi Li, and Sanjeev Katariya

A guest’s interaction with Airbnb doesn’t pause to wait for a nightly batch job. Someone might browse a dozen listings on a Tuesday afternoon, run a new search that evening, and expect the next search to reflect the recent activity; it’s also to Airbnb’s benefit for that to be the case. In our previous post, Personalizing Airbnb search by learning from the guest journey, we described how we built a Transformer-based sequence encoder that creates better, more personalized search rankings for a guest using the booking, review, and browsing data that is most relevant to them — their own. That system ran as a daily batch job: each night it processed the previous day’s activity and refreshed embeddings for guests who had something new to show for it.