After the deregulation of the airline industry in 1979, Sabre has played a pivotal role in enabling all aspects of a consumer’s travel by leveraging our core competencies in operations research, computer science and advanced data analytics to solve complex problems that generate revenue or lead to operational efficiencies that reduce costs. Core focus areas include flight scheduling, reservations, air shopping, retailing, airline pricing, revenue management, crew planning, airline operations, staff planning, cargo and customer service.

人工智能的概念应运而生,1950年,阿兰·图灵,写了他的里程碑式的论文“计算机器与智能” [1]。本文试图回答这个问题:“灿机想的?”这导致了图灵测试,这是一台机器的表现出的智能行为,这是相当于人的能力的考验。一些研究人员现在提出的Winograd架构测试作为一种替代[2]以及最近弗朗索瓦CHOLLET提出了一个叫做抽象和推理语料库基于算法信息论框架[3],他认为仅仅测量技术在任何给定的任务达不到衡量智力。

术语“人工智能”(AI)铸造了五年后,于1955年由约翰·麦卡锡教授谁度过了大部分职业生涯在斯坦福大学,开发了LISP,第二古老的高级语言(Fortran语言后)

人工智能作为一门学科是相当广泛,涵盖的机器能够在我们人类会考虑智能的方式来执行任务的概念。AI的应用程序的影响自然语言处理(例如机器翻译),机器视觉(如自动自动驾驶汽车)和机器学习(如电脑游戏)。机器学习是人工智能,其中计算机程序使用的数据,使他们能够学习,经过一段时间调整和细化的预测能力,而不是仅仅执行任务,他们已明确被编程做的一个子集。在早期,采用AI的很慢,它只是在过去十年中,特别是深层神经网络的出现,各种垂直行业,包括旅游,已采用基于人工智能技术。

Today we live in an AI and ML-enabled landscape. Many of the core advances made by Sabre in travel can now be enhanced with such Machine Learning algorithms. AI and ML are at the intersection of technologies that reason, interact and learn. At Sabre, we use such techniques end-to-end across marketing, interactive selling, fulfillment and customer care. We also leverage such algorithms to realize operational efficiencies within our complex internal systems that operate 24 x 7. In an AI-enabled landscape we can add capabilities for continuous learning without human intervention.

我们已经实施了一些价值主张概述如下:

Dynamic Thresholds for Service Health Portal

The Sabre Health Portal (SHP) developed internally monitors the health of various applications in real time and generates alerts based on dynamically adjusted thresholds. Since standard forecasting techniques do not deal well with volatility, we used a regression model combined with machine learning to generate dynamic confidence bands. Specifically, we found that a method called STL (Seasonal and Trend Decomposition using Loess) which uses the Loess smoother (Locally Estimated Scatterplot Smoothing) that uses local regression accounting for seasonality and trend was particularly effective. With such an approach, the detection of anomalies is also computationally fast. The benefits of dynamic thresholds in SHP is fewer false positives alerts, adjusted dynamically based on the change to metrics monitored by the minute.

酒店标识重复数据删除

A core capability of a marketplace like Sabre is the ability to offer rich content (such as hotel rates) from multiple sources. But different hotel aggregators (or suppliers) may not all representa hotel property同样的方式。姓名和地址的酒店contain errors, misspellings or transpositions of words. To integrate such content from different hotel aggregators requires a capability to ensure uniqueness of a property across aggregators (i.e. we need to be able to identify the “Marriott Marquis in Time Square New York” as the same property even though it may be represented differently by different aggregators). A combination of Logistic Regression, a rule-based approach for hotel attributes and Fuzzy String Matching is being used to solve the problem. TensorFlow Lattices [4] represent recent techniques that enable such identity correlations.

Demand Forecasting

Demand forecasting is dominated by time series models and customer choice modeling (CCM) techniques such as the Multinomial Logit (MNL) models. However, machine learning techniques such as Random Forest (RF), Gradient Boosted Trees (GBT), Support Vector Machines (SVM) and Artificial Neural Networks (ANN) can model non-linear relationships, allow for collinearity and offers flexibility to automatically learn customer behavior implicitly. Our results indicate that machine learning models can outperform traditional methods in some but not all cases.

知名度

无法使用的PNR(乘客姓名记录)的MRZ(机读区)的离港控制客户抬头的:从那些国家不是ICAO(国际民用航空组织)标准的原因两个问题上护照姓和名的识别无法在飞行通过APIS(先进的乘客信息系统)数据接近准确。名称识别问题是通过评估一系列的监督学习技术来解决。支持向量机和复发性神经网络具有〜80%的准确度进行最好的。

客户细分

With the age of intelligent retailing, airlines want to segment customers that go beyond traditional booking classes. Unsupervised learning techniques such as hierarchical clustering, k-means, sequential k-means, k-median, etc. can be used to create personas across a range of dimensions such as advance purchase, length of stay, mid-week vs weekend, number in party, length of haul, etc. Sabre has recently demonstrated such customer and trip-purpose segmentation in a manner that transparently augments input queries and output results, including New Distribution Capability (NDC) requests and responses.

测试和学习实验与多臂老虎机

A fundamental building block in intelligent retailing is a component that leverages a reinforcement learning technique called the multi-armed bandit (MAB) to manage online controlled experiments in order to learn customer behavior. The goal of the experiment is to find the best or most profitable action and the randomization distribution can be updated in real time as the experiment progresses. We also use this approach in air shopping experiments to control the cost of shopping with cache updates, measure shopping diversity that maximizes conversion rates, determine the best fare with schedule / fare / ancillary attributes to display on multi-month calendars, hotel retailing with offers on hotel websites, and to recommend air bundles (base fare + air ancillaries) to customers.

Other areas where we have found such MAB learning useful include persona-based recommendation engines for offers, personalization of offers, optimizing screen displays, detection of robotic shopping requests with pattern recognition, anomaly detection for hotel rates, chatbots, hotel product normalization, and fraud detection.

在军刀vwin电竞投注,我们的旅程,通过机器学习算法,我们提高AI和ML的整个组织中作用的认识是不断发展的沟通,在这里可以使用,鼓励团队学习和利用我们丰富的数据源,并提出新的与旅行相关的价值主张,增加价值给我们的客户。

References

  1. 图灵,A(1950);“美国计算机和情报,”心神, Volume 49, pp. 433-460. ‘
  2. 几何,赫克托耳(2011);”The Winograd Schema Challenge”,Commonsensereasoning.org
  3. CHOLLET,弗朗索瓦(2019);“在智力的测量”,https://arxiv.org/abs/1911.01547
  4. Gupta, Maya et al (2017): “TensorFlow Lattice: Flexibility Empowered by Prior Knowledge”,https://ai.googleblog.com/2017/10/tensorflow-lattice-flexibility.html