The Smart Instant Book Filter

Exploring a feature designed to help guests book with confidence

Our Mission

  1. When should we automatically apply smart Instant Book Filter
  2. How should we inform guests that an Instant Book filter has been turned on?

Architecture and Experience

Figure 1. Smart Instant Book Filter workflow
  1. Guest first lands on our platform.
  2. Guest searches for homes via the search box. The API service will validate basic information in this phase for example whether the query contains check-in and check-out dates to flag queries that are eligible for smart Instant Book filter.
  3. API server sends a search request with smart Instant Book filter eligibility flag to the Search service, which talks with Nebula — our in house storage platform, to perform ranking and generate search results. Our Decision Engine sits inside the Search service which consumes search results and uses a deterministic model to decide whether to trigger the smart Instant Book Filter.
  4. The Search service passes market availability metadata to the Market Insight service where candidate insights including the smart Instant Book Filter insight will be ranked based on their values to the guest.
  5. The Search service returns results to server.
  6. The Market Insight service returns final eligible insights to server.
  7. The API server combines all information and powers the front-end to display.


  • Real-time data: user data, platform data and query data are the fundamental information transferred from the API service. We’ve trained models on a per platform basis given various user experiences on different platforms and we respect last minute bookings differently by applying looser thresholds.
  • Aggregation data: we aggregate search results in terms of demand, supply and listings’ quantity and quality, together with other information including geographic drift, per search request. Listings’ quality is measured based on discounted cumulative utility which encapsulates the facts of each listing’s review rating, page view and other quality scores. Some of the aggregation data is pre-computed and supplied by an internal tool.

Algorithm and Model

Figure 2. Shift Request-to-Book to Instant Book
Figure 3. Instant Book score gain as a function of discounted utility value and distance ratio

User Experience

Smart Instant Book Filter with Tooltip
Contextual Market Insight Reminder Card

Key Takeaways

Rethinking the problem from the top funnel

Bridging the gap between guests and hosts

Future Plans



Creative engineers and data scientists building a world where you can belong anywhere.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store