Samsung Galaxy Unpacked 2026 is Feb. 25 Valve's Steam Machine: Everything we know Airbnb is testing out AI search with a'small percentage' of users Beyond improving search, Airbnb wants to lean heavily into artificial intelligence to help users with with booking, managing listings and customer service. Airbnb plans to double down on artificial intelligence to improve its user experience for both guests and hosts. During a fourth-quarter earnings call, Airbnb's CEO, Brian Chesky, said the company is building an AI-native experience aimed at helping guests book trips, assisting hosts with their listings, and running the company more efficiently. According to Chesky, there's an AI search tool to help guests book trips that's live for a small percentage of users right now. In a shareholder letter posted on Airbnb's website, the company said it's conducting early testing with an AI-powered search that is focused on giving guests a more natural way to describe what they're looking for, and ask questions about the listing and location.

Generating an abstraction of a dynamic domain that aligns with a given purpose remains a significant challenge given that the choice of such an abstraction can impact an agent's ability to plan, reason, and provide explanations effectively. We model the agent's concrete behaviors in PDDL and investigate the use of in-context learning with large language models (LLMs) for the generation of abstract PDDL domains and problem instances, given an abstraction objective specified in natural language. The benchmark examples we use are new and have not been part of the data any LLMs have been trained on. We consider three categories of abstractions: abstraction of choice of alternative concrete actions, abstraction of sequences of concrete actions, and abstraction of action/predicate parameters, as well as combinations of these. The generated abstract PDDL domains and problem instances are then checked by symbolic validation tools as well as human experts. Our experiments show that GPT -4o can generally synthesize useful planning domain abstractions in simple settings, although it is better at abstracting over actions than over the associated fluents.

It is increasingly important to generate synthetic populations with explicit coordinates rather than coarse geographic areas, yet no established methods exist to achieve this. One reason is that latitude and longitude differ from other continuous variables, exhibiting large empty spaces and highly uneven densities. To address this, we propose a population synthesis algorithm that first maps spatial coordinates into a more regular latent space using Normalizing Flows (NF), and then combines them with other features in a Variational Autoencoder (VAE) to generate synthetic populations. This approach also learns the joint distribution between spatial and non-spatial features, exploiting spatial autocorrelations. We demonstrate the method by generating synthetic homes with the same statistical properties of real homes in 121 datasets, corresponding to diverse geographies. We further propose an evaluation framework that measures both spatial accuracy and practical utility, while ensuring privacy preservation. Our results show that the NF+VAE architecture outperforms popular benchmarks, including copula-based methods and uniform allocation within geographic areas. The ability to generate geolocated synthetic populations at fine spatial resolution opens the door to applications requiring detailed geography, from household responses to floods, to epidemic spread, evacuation planning, and transport modeling.

As Brian Chesky tells it, the reinvention of Airbnb started with the coup at OpenAI. On November 17, 2023, the board of OpenAI fired company CEO Sam Altman. His friend Chesky leapt into action--publicly defending his pal on X, getting on the phone with Microsoft's CEO, and throwing himself into the thick of Altman's battle to retake OpenAI. Five days later Altman prevailed, and Chesky--"I was so jacked up," he says--turned his buzzing mind to his own company, Airbnb. The Chesky extended family had already held their turkey get-together a week earlier, and the Airbnb CEO had no holiday plan.

This research examines whether Airbnb guests' positive and negative comments influence acceptance rates and rental prices across six U.S. regions: Rhode Island, Broward County, Chicago, Dallas, San Diego, and Boston. Thousands of reviews were collected and analyzed using Natural Language Processing (NLP) to classify sentiments as positive or negative, followed by statistical testing (t-tests and basic correlations) on the average scores. The findings reveal that over 90 percent of reviews in each region are positive, indicating that having additional reviews does not significantly enhance prices. However, listings with predominantly positive feedback exhibit slightly higher acceptance rates, suggesting that sentiment polarity, rather than the sheer volume of reviews, is a more critical factor for host success. Additionally, budget listings often gather extensive reviews while maintaining competitive pricing, whereas premium listings sustain higher prices with fewer but highly positive reviews. These results underscore the importance of sentiment quality over quantity in shaping guest behavior and pricing strategies in an overwhelmingly positive review environment.

Airbnb is an online marketplace that connects hosts and guests to unique stays and experiences. When guests stay at homes booked on Airbnb, there are a small fraction of stays that lead to support needed from Airbnb's Customer Support (CS), which may cause inconvenience to guests and hosts and require Airbnb resources to resolve. In this work, we show that instances where CS support is needed may be predicted based on hosts and guests behavior. We build a model to predict the likelihood of CS support needs for each match of guest and host. The model score is incorporated into Airbnb's search ranking algorithm as one of the many factors. The change promotes more reliable matches in search results and significantly reduces bookings that require CS support.

Airbnb has announced it's deploying "anti-party technology" to prevent "unauthorized and disruptive parties" from happening at homes on its platform for New Year's Eve. If you were planning on hosting a get-together, start thinking of a Plan B. The company says it's using machine learning to identify and block high-risk, whole-home bookings in advance based on a variety of criteria. Airbnb's assessment takes into account things like the length of a trip, how far a listing is from your current location, and when you're trying to book to weed out potentially disruptive parties. The company will either block your reservation entirely or direct you to different accommodations. Party detection technology will be used in countries and regions globally, according to Airbnb.

Samantha posted her story on TikTok and shared the scenario on a private Facebook group; many women responded--including her date's wife. Ultimately, as a result of this conversation, Samantha decided to report his profile to Hinge. The next day, the company contacted her to let her know it would be deleting his profile. Mandy and Samantha were pleased with Bumble's and Hinge's swift action to take down the profiles of the men they had matched with--but the experience was indelible. Neither of them plans to use dating apps again.

Big changes could be coming to Airbnb next year. In a conversation at WIRED's Big Interview even in San Francisco on Tuesday, the company's cofounder and CEO Brian Chesky told global editorial director Katie Drummond that he hopes that, in 2025, "people say'that was one of the biggest reinventions of a company in recent memory.'" Though Chesky kept details scant, he did say that the company hopes to reimagine its Experiences section, which he says consumers really like but that he doesn't think has caught on as much as it could. The move seems to be an extension of Chesky's belief in the value of physical experiences and physical community, which he still thinks trump most digital experiences, even in the age of AI. In an effort to prove that, even two years into the AI revolution, fundamentally very little has been changed for most people, Chesky challenged the room to look at the apps on their phone home screens and think how much any of them have been substantially changed by generative AI.

The Airbnb search system grapples with many unique challenges as it continues to evolve. We oversee a marketplace that is nuanced by geography, diversity of homes, and guests with a variety of preferences. Crafting an efficient search system that can accommodate diverse guest needs, while showcasing relevant homes lies at the heart of Airbnb's success. Airbnb search has many challenges that parallel other recommendation and search systems but it has a unique information retrieval problem, upstream of ranking, called location retrieval. It requires defining a topological map area that is relevant to the searched query for homes listing retrieval. The purpose of this paper is to demonstrate the methodology, challenges, and impact of building a machine learning based location retrieval product from the ground up. Despite the lack of suitable, prevalent machine learning based approaches, we tackle cold start, generalization, differentiation and algorithmic bias. We detail the efficacy of heuristics, statistics, machine learning, and reinforcement learning approaches to solve these challenges, particularly for systems that are often unexplored by current literature.