Hotels


Breaking Through the Machine-Learning Hype

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While companies like Airbnb, sitting on huge cash reserves may find it easy to invest and experiment with these technologies, there's a popular perception that businesses with smaller budgets would find this difficult. Google's machine learning team has developed a technology called TensorFlow, which is a framework to implement machine learning at scale. Microsoft is another player in this market, providing a service called Azure Machine Learning Studio. On top of these three big corporations, there's a wide array of smaller startups providing machine learning services, among them BigML, MLJar and Algorithms.


Instagram turns to machine learning, Airbnb's rumored luxury rentals - Video

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Instagram turns to machine learning, Airbnb's rumored luxury rentals Today's top tech stories include Instagram's machine learning program that aims to block out offensive comments and Airbnb's rumored luxury rental service. Plus Apple's new OS gets previewed. Instagram turns to machine learning, Airbnb's rumored luxury rentals


AI, Machine Learning and Sentiment Analysis Applied to Finance – Millennium Hotel London Mayfair

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Artificial Intelligence, Machine Learning and Sentiment Analysis are changing the way in which numerous client services are offered. This trend is now being taken on board by multiple innovators: academia, start-ups, technology companies and financial market participants. AI and Machine Learning have emerged as a central aspect of analytics which is applied to multiple domains. AI and Machine Learning, Pattern classifiers and natural language processing (NLP) underpin Sentiment Analysis (SA); SA is a technology that makes rapid assessment of the sentiments expressed in news releases as well as other media sources such as Twitter and blogs.


Airbnb's Biggest Weapon Against Hotels: Machine Learning

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The company would prefer to also offer a booking experience that users will find more congenial and convenient. Consumers navigating a platform like Airbnb experience a catch-22, said Fontana: "Marketplaces are most useful when they have a lot of volume, because you can find exactly what you want, but marketplaces are also the most time-consuming and annoying when they have the most volume." One of the primary success metrics is the platform's conversion rate -- how many people make a booking. Curtis did reveal that introducing a deep neural net to the search-ranking system boosted Airbnb's recent conversion rate by 1 percent.


Flipboard on Flipboard

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The company would prefer to also offer a booking experience that users will find more congenial and convenient. Consumers navigating a platform like Airbnb experience a catch-22, said Fontana: "Marketplaces are most useful when they have a lot of volume, because you can find exactly what you want, but marketplaces are also the most time-consuming and annoying when they have the most volume." One of the primary success metrics is the platform's conversion rate -- how many people make a booking. Curtis did reveal that introducing a deep neural net to the search-ranking system boosted Airbnb's existing conversion rate by 1 percent.


Machine learning is driving growth at Airbnb

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It also uses host preferences to personalize search results for guests, promoting hosts likely to accept the accommodation request. This facilitates the arduous pricing process of looking at supply and demand in an area, among other market factors, and gives hosts a base to price from. It also uses host preferences to personalize search results for guests, promoting hosts likely to accept the accommodation request. This facilitates the arduous pricing process of looking at supply and demand in an area, among other market factors, and gives hosts a base to price from.


Airbnb's data science and machine learning efforts

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This model predicts the likelihood of a lodging listing getting booked for a certain day at a certain price based on factors like demand, listing location, listing type and quality. The second example is about understanding host preferences to predict if hosts accept visitors' accommodation requests. Airbnb built another machine learning model to predict the likelihood of a host accepting a visitor's accommodation inquiry. Hosts who have a higher probability of accepting an accommodation request will be ranked higher in search results.


7 ways AI will revolutionize business travel

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Hilton Worldwide contact centers are using AI and machine learning in hopes of creating a better customer experience, according to Andy Traba, VP of Behaviorial and Data Science for Mattersight, a behavioral routing software service Hilton is using. "When a business customer calls a Hilton hotel, Mattersight matches their data and analyzes their personality and behavior traits in less than five seconds," Traba explains. For example, Concur (developer of TripIt) has developed a chatbot for collaboration app Slack, enabling users to request information about their travel plans and submit expenses via Slack using a conversational interface, says Tim MacDonald, EVP of Global Products at Concur. We're excited to test what it means to bring voice-activated technology into the guest room, so guests can request services, learn about the local area, and perform general informational tasks like asking for the weather or setting an alarm for the next morning."


Alexa, can AI help improve the in-hotel experience?

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In room, there are a variety of possibilities, ranging from tablets and TVs to smart speakers and custom builds, running on platforms such as Amazon's Alexa Voice Service (AVS). Hotels may choose to use generic wake words such as Alexa and Siri, or create their own branded versions (Figure 3), built on platforms like IBM Watson, Amazon AVS, and Microsoft Cognitive Services. For large hotel chains, it's imperative to take into account the impact of regional data privacy laws vis-a-vis global guest experience ambitions. At the lower end, it can contribute towards more customized self-service experiences, while at the upper end it can support staff to deliver deeper and more personalized guest experiences.


The 5 key drivers of digital transformation today

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A few times each year, senior digital executives from around the world assemble at Forrester's Digital Transformation Summit to check in with each other and Forrester analysts to discuss the current state of digital evolution. Tyler McDaniel, VP Data Insights explained Forrester's model for the empowered customer, which divides customers into five tiers, from Progressive Pioneers to Reserved Resisters. Today, that insight needs to be implemented at several levels - understanding your customer base overall, understanding the needs of individual segments and, increasingly understanding the needs of each individual consumer so that their experience can be personalized. Achieving this level of insight means not only developing customer intuition and gathering anecdotes, but also leveraging data to continually feed organizations the pulse of our customers.