The hotel and should The hotel should be in the and should have If there is no such hotel, how about one that has Once you find the you want to book it for <4 people> and <2 nights> starting from Y ou are also looking for a . The restaurant should be in the price range and should serve food The restaurant should be Once you find the you want to book a table for at <12:15>(use veiled expression)> on (Background) Once you have made a booking, you do not want to give out your email address for receiving orders.

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.

Adverts by four of Britain's biggest hotel and travel firms have been banned for stating misleading minimum prices for rooms. The Advertising Standards Authority (ASA) upheld complaints against the Hilton hotel group, Travelodge, Booking.com and Accor over their use of eye-catching so-called from prices. The watchdog found only a small number of rooms actually available to book at the promoted price and concluded the adverts overstated the deals. It said this was unfair on those looking for good deals or seeking to make informed choices about where to book. ASA operations manager Emily Henwood said: Advertised prices must match what's really available.

The expansion of short-term rental platforms, such as Airbnb, has significantly disrupted local housing markets, often leading to increased rental prices and housing affordability issues. Accurately forecasting regional Airbnb market trends can thus offer critical insights for policymakers and urban planners aiming to mitigate these impacts. This study proposes a novel time-series forecasting framework to predict three key Airbnb indicators -- Revenue, Reservation Days, and Number of Reservations -- at the regional level. Using a sliding-window approach, the model forecasts trends 1 to 3 months ahead. Unlike prior studies that focus on individual listings at fixed time points, our approach constructs regional representations by integrating listing features with external contextual factors such as urban accessibility and human mobility. We convert structured tabular data into prompt-based inputs for a Large Language Model (LLM), producing comprehensive regional embeddings. These embeddings are then fed into advanced time-series models (RNN, LSTM, Transformer) to better capture complex spatio-temporal dynamics. Experiments on Seoul's Airbnb dataset show that our method reduces both average RMSE and MAE by approximately 48% compared to conventional baselines, including traditional statistical and machine learning models. Our framework not only improves forecasting accuracy but also offers practical insights for detecting oversupplied regions and supporting data-driven urban policy decisions.

In the hospitality industry, understanding the factors that drive customer review ratings is critical for improving guest satisfaction and business performance. This work proposes ReviewGraph for Review Rating Prediction (RRP), a novel framework that transforms textual customer reviews into knowledge graphs by extracting (subject, predicate, object) triples and associating sentiment scores. Using graph embeddings (Node2Vec) and sentiment features, the framework predicts review rating scores through machine learning classifiers. We compare ReviewGraph performance with traditional NLP baselines (such as Bag of Words, TF-IDF, and Word2Vec) and large language models (LLMs), evaluating them in the HotelRec dataset. In comparison to the state of the art literature, our proposed model performs similar to their best performing model but with lower computational cost (without ensemble). While ReviewGraph achieves comparable predictive performance to LLMs and outperforms baselines on agreement-based metrics such as Cohen's Kappa, it offers additional advantages in interpretability, visual exploration, and potential integration into Retrieval-Augmented Generation (RAG) systems. This work highlights the potential of graph-based representations for enhancing review analytics and lays the groundwork for future research integrating advanced graph neural networks and fine-tuned LLM-based extraction methods. We will share ReviewGraph output and platform open-sourced on our GitHub page https://github.com/aaronlifenghan/ReviewGraph

A convent turned hotel, Caribou Coffee, and progressive coworking space called The Coven--plus more things to see and do while on a business trip to Minneapolis and St. Paul. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Minnesota is the birthplace of the supercomputer, developed for code cracking during World War II. Tech giants of their day, including Cray Research and Control Data Corporation, were based in the Twin Cities.

Witnessed by the recent advancements on leveraging LLM for coding and multimodal understanding, we present WebGen-V, a new benchmark and framework for instruction-to-HTML generation that enhances both data quality and evaluation granularity. WebGen-V contributes three key innovations: (1) an unbounded and extensible agentic crawling framework that continuously collects real-world webpages and can leveraged to augment existing benchmarks; (2) a structured, section-wise data representation that integrates metadata, localized UI screenshots, and JSON-formatted text and image assets, explicit alignment between content, layout, and visual components for detailed multimodal supervision; and (3) a section-level multimodal evaluation protocol aligning text, layout, and visuals for high-granularity assessment. Experiments with state-of-the-art LLMs and ablation studies validate the effectiveness of our structured data and section-wise evaluation, as well as the contribution of each component. To the best of our knowledge, WebGen-V is the first work to enable high-granularity agentic crawling and evaluation for instruction-to-HTML generation, providing a unified pipeline from real-world data acquisition and webpage generation to structured multimodal assessment.

We study deploying large language models (LLMs) as business development (BD) agents for persuasive price negotiation in online travel agencies (OTAs), where aligning traveler affordability and hotel profitability directly affects bookings, partner relationships, and access to travel. The agent must follow a Standard Operating Procedure (SOP) while conducting multi-turn persuasion, interpreting colloquial inputs, and adhering to guardrails (no over-promising, no hallucinations). Conventional post-training -- supervised fine-tuning (SFT) or single-source reward optimization -- overfits scripts, misses nuanced persuasive style, and fails to enforce verifiable business constraints. We propose Reward-Enhanced Policy Optimization (REPO), a reinforcement learning post-training framework that aligns an LLM with heterogeneous rewards: a preference-trained reward model (RM) for dense human alignment, a reward judge (RJ) for high-level persuasive behavior and SOP compliance, and programmatic reward functions (RF) for deterministic checks on numerics, formatting, and guardrails. A straightforward enhancement mechanism is proposed to combine the RM with RJ and RF signals to curb reward hacking and improve negotiation quality. In production-style evaluations -- approximately 150 turns from real dialogues and 225 turns from curated bad-case dialogues -- REPO lifts average dialogue rating to 4.63: +1.20 over base, +0.83 over Direct Preference Optimization (DPO); +0.33 over Group Relative Policy Optimization (GRPO), increases the share of conversations with at least one excellent response to 66.67% (+23.34 percentage points over GRPO), and achieves a 93.33% bad-case fix rate with 75.56% clean fixes, outperforming SFT, DPO, PPO, and GRPO. We also observe emergent capabilities -- proactive empathy, localized reasoning, calibrated tactics -- that surpass gold annotations.

The hotel and should The hotel should be in the and should have If there is no such hotel, how about one that has Once you find the you want to book it for <4 people> and <2 nights> starting from Y ou are also looking for a . The restaurant should be in the price range and should serve food The restaurant should be Once you find the you want to book a table for at <12:15>(use veiled expression)> on (Background) Once you have made a booking, you do not want to give out your email address for receiving orders.

Evaluation plays a crucial role in the development of ranking algorithms on search and recommender systems. It enables online platforms to create user-friendly features that drive commercial success in a steady and effective manner. The online environment is particularly conducive to applying causal inference techniques, such as randomized controlled experiments (known as A/B test), which are often more challenging to implement in fields like medicine and public policy. However, businesses face unique challenges when it comes to effective A/B test. Specifically, achieving sufficient statistical power for conversion-based metrics can be time-consuming, especially for significant purchases like booking accommodations. While offline evaluations are quicker and more cost-effective, they often lack accuracy and are inadequate for selecting candidates for A/B test. To address these challenges, we developed interleaving and counterfactual evaluation methods to facilitate rapid online assessments for identifying the most promising candidates for A/B tests. Our approach not only increased the sensitivity of experiments by a factor of up to 100 (depending on the approach and metrics) compared to traditional A/B testing but also streamlined the experimental process. The practical insights gained from usage in production can also benefit organizations with similar interests.