Academic Publications & Airbnb Tech: 2025 Year in Review (opens in new tab)
Airbnb’s 2025 research program expanded across major academic venues, with a focus on applying AI, machine learning, and data science to search, ranking, personalization, and marketplace optimization. The company strengthened its presence at KDD and CIKM while broadening into NLP, optimization, and measurement science. Its research emphasized practical systems that improve experimentation, retrieval, recommendations, ranking quality, and booking conversion. ## Research Expansion in 2025 - Airbnb presented research at established conferences including KDD and CIKM, while expanding into venues such as COLING, LION, and VLDB. - Researchers used these conferences to: - Share production-scale findings with academic and industry peers. - Develop new collaborations. - Learn about emerging methods. - Mentor early-career researchers. - The work was organized around themes including applied machine learning for search and personalization, and NLP and LLM systems in production. ## Search Ranking and Marketplace Retrieval at KDD KDD is a leading conference for data mining, knowledge discovery, and large-scale analytics. Airbnb has presented there since 2018, and its 2025 contributions focused on improving search experimentation and location retrieval. - **Interleaving and counterfactual evaluation** - Airbnb introduced techniques for evaluating search-ranking ideas before launching full A/B tests. - These methods help teams identify promising experiments more quickly. - They are especially useful for accommodation booking, where long conversion cycles can make statistical significance slow to achieve. - The goal is to accelerate experimentation without compromising evaluation accuracy. - **Extreme classification for audience expansion** - Airbnb presented a high-precision approach to retrieving relevant geographic areas in its two-sided marketplace. - The system uses categorical location cells to identify areas containing listings a guest might realistically book. - This helps balance Airbnb’s diverse global inventory with users’ preferences for location, amenities, style, and price. - Airbnb also presented work on **TSMO**, or Two-sided Marketplace Optimization, and indicated that some technologies might eventually be open-sourced. ## Search and Recommendation Advances at CIKM At CIKM 2025, Airbnb’s Relevance and Personalization team had five peer-reviewed papers accepted. The work addressed search, recommendations, ranking metrics, multimodal representations, and user comparison behavior. - **Recommendations for overly narrow searches** - Airbnb developed a system that suggests alternatives when a guest’s search returns too few accommodations. - Suggestions may include different dates, broader amenity requirements, or adjusted price ranges. - The system aims to reduce search frustration and increase booking rates. - **Map-specific ranking optimization** - Maps account for approximately 80% of Airbnb search interactions. - Traditional feed-ranking assumptions do not accurately represent how users view and interact with map results. - Airbnb introduced a map-specific version of NDCG, a ranking-quality metric. - Experiments showed that optimizing for this metric improved bookings. - **BiListing multimodal embeddings** - BiListing combines listing text and photos into unified embedding representations. - It uses large language models and pretrained language-image models as ranking signals. - The approach produced a reported 0.425% improvement in normalized discounted booking gain and generated tens of millions of dollars in incremental revenue. - **Beyond pairwise learning-to-rank** - Airbnb proposed a learning-to-rank method that models interactions between items during pairwise comparisons. - This provides a more realistic view of what users prefer when evaluating search results. - The paper also describes efficient implementation techniques and online and offline evaluation results. - **Learning to Comparison-Shop** - The LTCS system models how users compare multiple listings rather than evaluating each listing independently. - It produced statistically significant improvements of 1.7% in NDCG and 0.6% in booking conversion rate. - The work reflects Airbnb’s broader effort to make ranking models sensitive to the context of an entire results page. ## NLP and Production LLM Systems - Airbnb also highlighted NLP and production LLM research, including participation in EMNLP. - Relevant application areas include: - Customer support. - Search and discovery. - Trust and safety. - EMNLP covers language-model architectures, training strategies, safety, evaluation, datasets, and open-source tooling. Airbnb’s 2025 publications show a strong emphasis on research that translates directly into marketplace performance. The most practical opportunities involve faster experimentation, context-aware ranking, multimodal listing understanding, and recommendation systems that help guests recover from overly restrictive searches.