We develop pre-trained estimators for structural econometric models. The estimator uses a neural net to recognize the structural model's parameter from data patterns. Once trained, the estimator can be shared and applied to different datasets at negligible cost and effort. Under sufficient training, the estimator converges to the Bayesian posterior given the data patterns. As an illustration, we construct a pretrained estimator for a sequential search model (available at pnnehome.github.io). Estimation takes only seconds and achieves high accuracy on 12 real datasets. More broadly, pretrained estimators can make structural models much easier to use and more accessible.

You Won't Be Able to Offload Your Holiday Shopping to AI Agents Anytime Soon Chatbot developers and retail giants are battling over user data as they lay the foundation for a future in which AI agents can do all your online shopping for you. Ask OpenAI's ChatGPT about a product on Etsy, and chances are you can enter your payment details and buy it without ever leaving the app. Instant Checkout was one of the first features to emerge from a recent wave of partnerships between leading AI and ecommerce companies. The aim is to encourage people to hand off parts of the browsing and ordering experience to AI tools and usher in an era of agentic shopping. But while these so-called agents have started to become more commonplace, they are far from taking over as full-time virtual buyers. OpenAI, Google, Amazon, and other AI chatbot developers are still negotiating with major retail partners on the best way to limit costly mistakes by agents and the amount of product data and chat history that have to be exchanged to make these agents successful, according to executives at seven tech and ecommerce companies who spoke with WIRED.

Flights are unbelievably expensive, with many airliners offering their highest airfares in years. Rising jet fuel prices, staff shortages due to pandemic disruptions and a lack of new aircraft are creating a perfect storm of problems for passengers. But there are hidden ways that you can keep the cost of your summer travel down, according to experts at Expedia. It pays to know what day of the week is best to fly along with how far in advance to purchase a ticket, too. The best time to book a domestic flight is at least a month before.

We've seen success with our AI powered platform across a variety of brands from Expedia and ANA Japan to OLA and TikTok. As part of building a global in-house studio to support their growth post-covid, Expedia were searching for a tool that could organise workflows, unite remote teams and drive down costs in production. IQ provides the backbone to Expedia's new in-house studio facilitating end to end production workflow and remote production commissioning, showing a'single master view' of everything going on across their production teams. IQ has decreased the time it takes to deliver projects, increasing communication and collaboration across international teams, and removing the need for complicated email feedback and WeTransfer.

Historically, we carried out content moderation using third party vendors, but with the increasing volume of the images (and text content) we started to automate as much of this work as possible with the help of machine learning models. In the next few sections, we will provide an overview of our modeling framework, data collection, and evaluation frameworks. One challenge we faced when we started this project was the lack of enough labeled data with granular categories for user generated content. In the past, Expedia teams labeled content using crowd-sourcing, but in many cases we found that images had only been labeled as approved or rejected without specifying the reason. This meant we lacked the training data to inform models why an image was rejected (an image can be rejected because it had low quality, or because it contains identifiable children, or for many other reasons).

Travel chatbots are changing the tourism and transportation industries the same way that Kayak, Travelocity, and Expedia changed the landscape of bookings 15 years ago. They're becoming an essential tool for travel companies to stay competitive. Tourism chatbots address two major challenges that travel companies face today: rising operational costs and growing customer service expectations. As the travel and tourism sectors continue to grow, so do operating costs. Fuel prices are rising, real estate is appreciating, and labor gaps are driving up wages. Travel chatbots help companies find relief by creating more efficient customer service teams.

One of the first things to do while planning a trip is to book a good place to stay. Booking a hotel online can be an overwhelming task with thousands of hotels to choose from, for every destination. Motivated by the importance of these situations, we decided to work on the task of recommending hotels to users. W e used Expedia's hotel recommendation dataset, which has a variety of features that helped us achieve a deep understanding of the process that makes a user choose certain hotels over others. The aim of this hotel recommendation task is to predict and recommend five hotel clusters to a user that he/she is more likely to book given hundred distinct clusters.

Hotels.com, part of the world's largest online travel company Expedia, wants to power all of its decisions with machine learning, with the support of Amazon Web Services (AWS). Expedia started using AWS back in 2013 to speed up various large-scale projects using capacity and traffic management. It has since been moving the vast majority of its workloads to AWS, and at AWS re:Invent last year, the company announced that it would be going all-in on AWS. That will include standardizing its use of AWS machine learning technologies across all of its brands, including Expedia.com, Speaking to diginomica at AWS Summit London last month, Fryer explains that this will mean all of Expedia group's applications, websites and products, along with supporting technologies such as those focused on data and machine learning, will move from its data centers over a period of time onto the AWS technology stack.

We are looking for top-notch applied researchers and scientists interested in breaking new grounds to solve some of the most complex computational problems in the marketing domain. The focus of your job will be in developing state of the art machine learning algorithms to power various aspects of highly complex global marketing campaigns. We spend hundreds of millions of dollars participating in highly dynamic and competitive auctions in travel Metasearch sites (TripAdvisor, Kayak, Trivago), Paid Search (Google, Bing, Yahoo), Display Ads/Programmatic as well as in social media (Facebook). These marketing channels would benefit from solving a range of fundamental problems with varying levels of complexity, e.g., from inference problems that arise due to sparse data to solving complex optimization problems impacted by dynamic market and user behavior. We have rich, massive, and untapped data sets containing observation points that can be collected only by spending huge amounts of money.

President Donald Trump has signed into law a $1.5 trillion tax overhaul package. Trump touted the size of the tax cut, declaring to reporters in the Oval Office before he signed it Friday that'the numbers will speak.'