# learning-to-rank

Published articles for learning-to-rank.

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## Building Fair and Personalized Flight Recommenders: Addressing Distortions, Outliers and Evolving...

DevFeed: [Building Fair and Personalized Flight Recommenders: Addressing Distortions, Outliers and Evolving...](<https://devfeed.tech/articles/building-fair-and-personalized-flight-recommenders-addressing-distortions-outliers-and-evolving-109443.md>)

Original publisher: [Read original article](<https://medium.com/amex-gbt-technology/building-fair-and-personalized-flight-recommenders-addressing-distortions-outliers-and-evolving-9f8ed6a661b2?source=rss----60a0578f4096---4>)

Author: Christian Tazengou

Published: 2026-10-30T14:28:13Z

Content type: article

Language: en

Sources: [Amex GBT Technology](<https://devfeed.tech/sources/amex-gbt-technology.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [personalization](<https://devfeed.tech/topics/personalization.md>), [Feature Engineering](<https://devfeed.tech/topics/feature-engineering.md>), [ai-governance](<https://devfeed.tech/topics/ai-governance.md>)

Tags: [american-express](<https://devfeed.tech/tags/american-express.md>), [amex](<https://devfeed.tech/tags/amex.md>), [analysis](<https://devfeed.tech/tags/analysis.md>), [approach](<https://devfeed.tech/tags/approach.md>), [automatically](<https://devfeed.tech/tags/automatically.md>), [based](<https://devfeed.tech/tags/based.md>), [behaviors](<https://devfeed.tech/tags/behaviors.md>), [best](<https://devfeed.tech/tags/best.md>), [better](<https://devfeed.tech/tags/better.md>), [booking](<https://devfeed.tech/tags/booking.md>), [building](<https://devfeed.tech/tags/building.md>), [business](<https://devfeed.tech/tags/business.md>), [business-travel](<https://devfeed.tech/tags/business-travel.md>), [but](<https://devfeed.tech/tags/but.md>), [can](<https://devfeed.tech/tags/can.md>), [client](<https://devfeed.tech/tags/client.md>), [comes](<https://devfeed.tech/tags/comes.md>), [company](<https://devfeed.tech/tags/company.md>), [flight](<https://devfeed.tech/tags/flight.md>), [learning-to-rank](<https://devfeed.tech/tags/learning-to-rank.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [outliers](<https://devfeed.tech/tags/outliers.md>), [personalization](<https://devfeed.tech/tags/personalization.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>)

### AI overview

The article describes Amex GBT's flight recommender as a Learning to Rank system trained on booking data and user feedback. LambdaRank uses pairwise lambdas weighted by each swap's effect on NDCG; LambdaMART applies this approach with boosted decision trees. Ranking features include fare, schedule, loyalty, stops, policy, and travel history. To reduce distortion, the described process detects anomalies with Isolation Forest, stratifies training data, reviews potentially skewed features, and monitors results across traveler and route segments. Personalized airline, airport, and timing preferences are built at individual, company, and traveler-group levels, with time decay giving recent choices more weight. The article reports internal offline evaluation gains in top-result ranking quality when personalization was added to distortion-corrected training data; correction alone had smaller and less consistent effects.

### Source excerpt

Building Fair and Personalized Flight Recommenders: Addressing Distortions, Outliers and Evolving PreferencesDiscover how American Express Global Business Travel (Amex GBT) utilizes machine learning to deliver personalized flight rankings for travelers. Business travelers expect booking to be quick, easy, and tailored to their busy lives. For a leading business travel company like American Express Global Business Travel (Amex GBT), giving travelers the best flight options isn't just a feature; it's a fundamental necessity that drives client satisfaction, productivity, and long-term loyalty. Why machine learning is important for better personalization Traditional rule-based models and classical approaches like Multi-Criteria Decision Analysis (MCDA) can provide some guidance for recommending flights, but they often fall short in capturing the nuance and diversity of traveler preferences. Rule-based systems rely on rigid "if-then" logic, while MCDA uses predefined weights and criteria to rank options. Both methods lack the ability to adapt dynamically to changing user behaviors and new data. With business travelers' needs constantly evolving, a more flexible and data-driven solution is essential. This is where machine learning comes in. By analyzing large volumes of real booking data, machine learning models can automatically uncover patterns and preferences, allowing for far more accurate and personalized recommendations. One effective approach in this space is the use of Learning to Rank models, which are specifically designed to order search results or recommendations based on what matters most to each traveler. Optimizing ranking with lambda gradients Traditional ranking models often use a pointwise approach, where each flight is scored individually and then ranked based on those scores. While this method is straightforward, it overlooks how options relate to each other, a key consideration for traveler preferences that are inherently comparative. For example, con