Optimizing Airbnb Search Journey with Multi-task Learning (SIGKDD 2023)
This post indexes the KDD 2023 paper "Optimizing Airbnb Search Journey with Multi-task Learning" from Airbnb, published in the proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
- Venue: SIGKDD 2023 (Applied Data Science track)
- Source: https://dl.acm.org/doi/abs/10.1145/3580305.3599881
- Topic area: Search / Recommendation / Personalization, multi-task learning
- Journey-level optimization: The paper moves beyond optimizing individual search sessions or query-level ranking, instead modeling the full user search journey on Airbnb — from initial exploration to booking.
- Multi-task learning: Multiple objectives along the journey are learned jointly within a shared model, rather than training separate single-objective rankers.
- Industrial deployment: The approach was integrated into Airbnb's large-scale search ranking stack and validated with online experiments, following the applied-data-science tradition of prior Airbnb search papers (e.g., applied embedding techniques and deep learning ranking at KDD 2018–2019).
- Architecture: Shared-bottom or gated multi-task architectures can serve multiple journey objectives in a single forward pass, controlling serving latency.
- Data: Sequential user behavior logs (impressions, clicks, wishlists, bookings) are the core training signal; label definitions across the journey matter greatly.
- Evaluation: Offline ranking metrics must be complemented by online A/B tests measuring journey-level and long-term outcomes.
- Airbnb's earlier KDD papers on embedding-based personalization and deep learning for search ranking
- Multi-task and multi-objective ranking literature in e-commerce and ads (e.g., MMoE-style architectures, conversion prediction)
Key points
Context
Most learning-to-rank systems optimize per-request metrics such as booking probability for a single query. Airbnb's journey framing reflects a broader industry trend: optimizing cumulative, multi-session outcomes (discoverability, engagement, and eventual conversion) rather than myopic per-impression objectives. Multi-task learning enables the ranker to balance short-term signals (clicks) against longer-term goals (wishlist adds, contact, booking) within one architecture.
Practical implications
> Note: This index post summarizes the paper's public metadata and known context. For exact architecture details, training recipes, and quantitative online experiment results, refer to the original PDF via the ACM Digital Library link above.