Enhancing travel recommendations: Ai-driven personalization through user digital footprints

Esta tesis tiene como objetivo examinar la manera en que la huella digital que dejan los usuarios en internet puede utilizarse para optimizar la personalización de los servicios turísticos, mediante el uso de inteligencia artificial. El documento propone que el auge de la inteligencia artificial ha abierto un mundo de oportunidades para desarrollar nuevas herramientas para mejorar la experiencia de viaje digital. El enfoque se basa en la idea de que las huellas digitales son únicas y particulares de cada individuo y estos valiosos datos pueden dar lugar a sugerencias de viaje más inteligentes y certeras. Se consideran las actitudes de comportamiento del usuario, como la influencia del contenido generado por el usuario en las redes sociales y el boca a boca electrónico en el proceso de planificación del viaje, así como las implicaciones de este rastro de datos en la optimización de los servicios de viaje personalizados. Este modelo describe la relación entre la inteligencia artificial y la hiper personalización de servicios. Como es una tendencia creciente que está alterando nuestra realidad actual, la tesis presentada desarrolla una aplicación de viajes a medida que, con el permiso del usuario, aprovecha los datos recopilados de las redes sociales personales para construir un plan de viaje específico basado en las preferencias individuales.This thesis aims to examine the way the digital footprint users leave behind can be utilized to optimize the personalization of tourism services, through the use of artificial intelligence. The paper proposes that the surge of artificial intelligence has opened a world of opportunities to develop new tools to improve the digital travel experience. The approach is based on the idea that digital footprints are unique and particular to each individual and this valuable data can result in smarter and unerring travel suggestions. Behavioral attitudes of the user, such as the influence of user-generated content in social media and e-word of mouth in the travel planning process, are considered, as well as the implications of this data trail in the optimization of customized travel services. This model describes the relationship between artificial intelligence and hyper-personalization of services. As it is a growing trend that is disrupting our current reality, the presented thesis develops a tailor-made traveling application that, with permission of the user, leverages the data collected from personal social media to build a specific travel plan based on each user’s preferences

Dynamic Airline Pricing and Seat Availability: Evidence from Airline Markets

Airfares fluctuate due to demand shocks and intertemporal variation in willingness to pay. I estimate a model of dynamic airline pricing accounting for both sources of price adjustments using flight-level data. I use the model estimates to evaluate the welfare effects of dynamic airline pricing. Relative to uniform pricing, dynamic pricing benefits early-arriving, leisure consumers at the expense of late-arriving, business travelers. Although dynamic pricing ensures seat availability for business travelers, these consumers are then charged higher prices. When aggregated over markets, welfare is higher under dynamic pricing than under uniform pricing. The direction of the welfare effect at the market level depends on whether dynamic price adjustments are mainly driven by demand shocks or by changes in the overall demand elasticity

Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating

Employing customer information from one of the world's largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer's acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years mirroring actual data indicates that PREM implementation results in approximately 1.12 million (7.94%) fewer non-relevant customer email messages, a predicted increase of 72,200 (37.2%) offers accepted, and an estimated $72.2 million (37.2%) of increased revenue. Our results illustrate the potential of automated pricing information and targeting marketing messages for upselling acceptance. We also identified three customer segments: (1) Never Upgrades are those who never take the upgrade offer, (2) Upgrade Lovers are those who generally upgrade, and (3) Upgrade Lover Lookalikes have no historical record but fit the profile of those that tend to upgrade. We discuss the implications for airline companies and related travel and tourism industries.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Essays on pricing under uncertainty

This dissertation analyzes pricing under uncertainty focusing on the U.S. airline industry. It sets to test theories of price dispersion driven by uncertainty in the demand by taking advantage of very detailed information about the dynamics of airline prices and inventory levels as the flight date approaches. Such detailed information about inventories at a ticket level to analyze airline pricing has been used previously by the author to show the importance of capacity constraints in airline pricing. This dissertation proposes and implements many new ideas to analyze airline pricing. Among the most important are: (1) It uses information about inventories at a ticket level. (2) It is the first to note that fare changes can be explained by adding dummy variables representing ticket characteristics. Therefore, the load factor at a ticket level will lose its explanatory power on fares if all ticket characteristics are included in a pricing equation. (3) It is the first to propose and implement a measure of Expected Load Factor as a tool to identify which flights are peak and which ones are not. (4) It introduces a novel idea of comparing actual sales with average sales at various points prior departure. Using these deviations of actual sales from sales under average conditions, it presents is the first study to show empirical evidence of peak load pricing in airlines. (5) It controls for potential endogeneity of sales using dynamic panels. The first essay tests the empirical importance of theories that explain price dispersion under costly capacity and demand uncertainty. The essay calculates a measure of an Expected Load Factor, that is used to calibrate the distribution of demand uncertainty and to identify which flights are peak and which ones are off-peak. It shows that different prices can be explained by the different selling probabilities. The second essay is the first study to provide formal evidence of stochastic peak-load pricing in airlines. It shows that airlines learn about the demand and respond to early sales setting higher prices when expected demand is high and more likely to exceed capacity

Scalable Extraction of Training Data from (Production) Language Models

This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training dataset. We show an adversary can extract gigabytes of training data from open-source language models like Pythia or GPT-Neo, semi-open models like LLaMA or Falcon, and closed models like ChatGPT. Existing techniques from the literature suffice to attack unaligned models; in order to attack the aligned ChatGPT, we develop a new divergence attack that causes the model to diverge from its chatbot-style generations and emit training data at a rate 150x higher than when behaving properly. Our methods show practical attacks can recover far more data than previously thought, and reveal that current alignment techniques do not eliminate memorization

Dynamic Airline Pricing and Seat Availability

Airfares fluctuate over time due to both demand shocks and intertemporal variation in willingness to pay. I develop and estimate a model of dynamic airline pricing accounting for both forces with new flight-level data. With the model estimates, I disentangle key interactions between the arrival pattern of consumer types and scarcity of remaining capacity due to stochastic demand. I show that dynamic airline pricing expands output by lowering fares charged to early-arriving, price-sensitive customers. It also ensures seats for late-arriving travelers with the highest willingness to pay (e.g. business travelers) who are then charged high prices. I find that dynamic airline pricing increases total welfare relative to a more restrictive pricing regime. Finally, I show that abstracting from stochastic demand results in incorrect inferences regarding the extent to which airlines utilize intertemporal price discrimination

INTERRELATIONSHIPS BETWEEN USERS AND SYSTEM FLEXIBILITIES WITH PERCEIVED USABILITY OF ONLINE AIRLINE RESERVATION SYSTEMS

It is very critical for the organizations to design flexible systems that are easy to use and can accomplish all the requirements by way of offering customizability. Philosophers argue that users are good in adapting the systems; however, research shows users dissatisfaction with existing Online Airline Reservation Systems in terms of task completion. Therefore, researchers are eager to find out ways for improving online usability of the systems, how users' Perceived Usability of the system is formulated by its flexibility functions. This research therefore examines travelers' expectations, preferences and online behavior (Users' Flexibility) and aligns that with designing of flexible online airline reservation systems (System's Flexibility) and users' as evaluators of the online systems to determine its Perceived Usability through users' effectiveness, efficiency and satisfaction (Perceived Usability). In this dissertation, both quantitative and qualitative techniques were used to analyze the data collected in the context of SF, lJF and PU of the systems. A redesign solution for enhanced usability was developed based on HCI guidelines and the flexibility tactics used in online travel agencies, which led to a proposed interface with the integration of opaque mechanism. The two interfaces were used in the experiment. Participants were requested to complete the evaluation of the existing and proposed interfaces. The findings suggested that users can be classified on the basis of their Flexible Traveling Behavior which led to the development of a Users' Flexibility measuring scale. It is further investigated that integration of opaque fares concept would increase the usability of the system. Since flexibility is referred to its ability to respond to internal or external changes, systems incorporated with opaque fares would serve the role of external change agent by way of providing flexibility in users' decision making and will also serve the role of internal change agent by way of providing the capability of accepting changed decisions