Collaborating Authors


Personalized Ranking Model for Lodging


The Egencia (an Expedia Group company) data science team builds AI into its platform in various different ways to create experiences that are personalized for travelers and travel managers. One of our approaches is to personalize flight and hotel search results for travelers who book on our platform. Recently, we've published how the Egencia Smart Mix Flight ranking model has helped personalize flight search results. Below, we discuss Egencia's personalized Smart Mix Lodging ranking model that enhances user experience and efficiency in hotel search, as well as booking. Egencia's hotel search UI provides users with the flexibility to set their preferences in a variety of ways, such as in-policy rates, price range, minimum star rating, amenities, etc.

Bayesian Topic Regression for Causal Inference Machine Learning

Causal inference using observational text data is becoming increasingly popular in many research areas. This paper presents the Bayesian Topic Regression (BTR) model that uses both text and numerical information to model an outcome variable. It allows estimation of both discrete and continuous treatment effects. Furthermore, it allows for the inclusion of additional numerical confounding factors next to text data. To this end, we combine a supervised Bayesian topic model with a Bayesian regression framework and perform supervised representation learning for the text features jointly with the regression parameter training, respecting the Frisch-Waugh-Lovell theorem. Our paper makes two main contributions. First, we provide a regression framework that allows causal inference in settings when both text and numerical confounders are of relevance. We show with synthetic and semi-synthetic datasets that our joint approach recovers ground truth with lower bias than any benchmark model, when text and numerical features are correlated. Second, experiments on two real-world datasets demonstrate that a joint and supervised learning strategy also yields superior prediction results compared to strategies that estimate regression weights for text and non-text features separately, being even competitive with more complex deep neural networks.

With One Voice: Composing a Travel Voice Assistant from Re-purposed Models Artificial Intelligence

Voice assistants provide users a new way of interacting with digital products, allowing them to retrieve information and complete tasks with an increased sense of control and flexibility. Such products are comprised of several machine learning models, like Speech-to-Text transcription, Named Entity Recognition and Resolution, and Text Classification. Building a voice assistant from scratch takes the prolonged efforts of several teams constructing numerous models and orchestrating between components. Alternatives such as using third-party vendors or re-purposing existing models may be considered to shorten time-to-market and development costs. However, each option has its benefits and drawbacks. We present key insights from building a voice search assistant for search and recommendation system. Our paper compares the achieved performance and development efforts in dedicated tailor-made solutions against existing re-purposed models. We share and discuss our data-driven decisions about implementation trade-offs and their estimated outcomes in hindsight, showing that a fully functional machine learning product can be built from existing models.

Machine Learning Real World projects in Python


Machine Learning is one of the hottest technology field in the world right now! This field is exploding with opportunities and career prospects. Machine Learning techniques are widely used in several sectors now a days such as banking, healthcare, finance, education transportation and technology. This course covers several technique in a practical manner, the projects include coding sessions as well as Algorithm Intuition: So, if you've ever wanted to play a role in the future of technology development, then here's your chance to get started with Machine Learning. Because in a practical life, machine learning seems to be complex and tough,thats why we've designed a course to help break it down into real world use-cases that are easier to understand.

Doctor GPT-3: hype or reality? - Nabla


You may have heard about GPT-3 this summer, the new cool kid on the AI block. GPT-3 came out of OpenAI, one of the top AI research labs in the world which was founded in late 2015 by Elon Musk, Sam Altman and others and later backed with a $1B investment from Microsoft. You've probably also heard about the ongoing AI revolution in healthcare, thanks to promising results in areas such as automated diagnosis, medical documentation and drug discovery, to name a few. Some have claimed that algorithms now outperform doctors on certain tasks and others have even announced that robots will soon receive medical degrees of their own! This can all sound far-fetched... but could this robot actually be GPT-3?

Probing Task-Oriented Dialogue Representation from Language Models Artificial Intelligence

This paper investigates pre-trained language models to find out which model intrinsically carries the most informative representation for task-oriented dialogue tasks. We approach the problem from two aspects: supervised classifier probe and unsupervised mutual information probe. We fine-tune a feed-forward layer as the classifier probe on top of a fixed pre-trained language model with annotated labels in a supervised way. Meanwhile, we propose an unsupervised mutual information probe to evaluate the mutual dependence between a real clustering and a representation clustering. The goals of this empirical paper are to 1) investigate probing techniques, especially from the unsupervised mutual information aspect, 2) provide guidelines of pre-trained language model selection for the dialogue research community, 3) find insights of pre-training factors for dialogue application that may be the key to success.

DLGNet-Task: An End-to-end Neural Network Framework for Modeling Multi-turn Multi-domain Task-Oriented Dialogue Artificial Intelligence

Task oriented dialogue (TOD) requires the complex interleaving of a number of individually controllable components with strong guarantees for explainability and verifiability. This has made it difficult to adopt the multi-turn multi-domain dialogue generation capabilities of streamlined end-to-end open-domain dialogue systems. In this paper, we present a new framework, DLGNet-Task, a unified task-oriented dialogue system which employs autoregressive transformer networks such as DLGNet and GPT-2/3 to complete user tasks in multi-turn multi-domain conversations. Our framework enjoys the controllable, verifiable, and explainable outputs of modular approaches, and the low development, deployment and maintenance cost of end-to-end systems. Treating open-domain system components as additional TOD system modules allows DLGNet-Task to learn the joint distribution of the inputs and outputs of all the functional blocks of existing modular approaches such as, natural language understanding (NLU), state tracking, action policy, as well as natural language generation (NLG). Rather than training the modules individually, as is common in real-world systems, we trained them jointly with appropriate module separations. When evaluated on the MultiWOZ2.1 dataset, DLGNet-Task shows comparable performance to the existing state-of-the-art approaches. Furthermore, using DLGNet-Task in conversational AI systems reduces the level of effort required for developing, deploying, and maintaining intelligent assistants at scale.

A Detailed Guide to Chatbot In 2020


It is no doubt that Chatbots alias virtual assistants are creating wonders in the technology space. It has transformed the technology landscape to its next level and has brought a tremendous revolution in its respective industries. This article deals with more of chatbots, its nitty-gritty aspects, and the industries that are about to get revolutionized because of chatbots. Chatbots are nothing but computer programs, which are mostly used across various industries to have productive conversations with its customers. The mode of conversation carried over by chatbots can be of different types.

PriceAggregator: An Intelligent System for Hotel Price Fetching Machine Learning

This paper describes the hotel price aggregation system - PriceAggregator, deployed at Agoda, a global online travel agency for hotels, vacation rentals, flights and airport transfer. Agoda aggregates non-direct suppliers' hotel rooms to ensure that Agoda's customers always have the widest selection of hotels, room types and packages. As of today, Agoda aggregates millions of hotels. The major challenge is that each supplier only allows Agoda to fetch for the hotel price with a limited amount of Queries Per Second (QPS). Due to the sheer volume of Agoda's user search traffic, this limited amount of QPS is never enough to cover all user searches. Inevitably, many user searches have to be ignored. Hence, booking lost. To overcome the challenge, we built PriceAggregator. PriceAggregator intelligently determines when, how and what to send to the suppliers to fetch for price. In this paper, we not only prove PriceAggregator is optimal theoretically but also demonstrate that PriceAggregator performs well in practice. PriceAggregator has been deployed in Agoda. Extensive online A/B experimentation have shown that PriceAggregator increases Agoda's bookings significantly.

Four Ways in Which Chatbots Can Help the Hotel Industry - The Chatbot


Chatbots are the topic of discussion in every hotel conference and hospitality article. This is because of what they can accomplish for the industry. Presently, there are thousands of hotels that are already offering messaging services to their guests, some are offering the option to text them directly while some have developed their own apps, and the rest are utilizing third-party messaging channels like Whatsapp and Facebook. Looking closely at this technology trend, it seems like your next brand interaction will likely not be with a human being. In fact, Gartner Predicts, 85% of the interaction will be managed without a live person.