Personalise flight recommendations to target individual consumers in time sensitive and dynamic market
Transportation & LogisticsAI ApplicationsAI Productivity
The provided sources do not contain specific information on personalizing flight recommendations for time-sensitive and dynamic markets. While the sources discuss general principles of personalization—such as using agentic infrastructure for marketing [4], leveraging LLMs for behavioral profiling in ride-hailing [8], and managing recommendations for scarce, short-lived opportunities in spot-work platforms [5]—they do not address the airline industry or flight-specific recommendation strategies.
Sources
- Understanding Guest Preferences and Optimizing Two-sided Marketplaces: Airbnb as an Example — Arxiv
- From Prompt to Purchase: How AI Brand Recommendations Move Consumers on the Open Web — Arxiv
- The Value of Personalized Recommendations: Evidence from Netflix — Arxiv
- Sustained Impact of Agentic Personalisation in Marketing: A Longitudinal Case Study — Arxiv
- Designing Recommendation Exposure and Favorite Lists: A Field Experiment in a Spot-Work Platform — Arxiv
- AI Transforms Mother's Day Shopping — Daily Brew
- Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation — Arxiv
- ProfiLLM: Utility-Aligned Agentic User Profiling for Industrial Ride-Hailing Dispatch — Arxiv
- Why personalised pricing could be a good deal for shoppers — FT
- Unintended Consequences of Recommender System Interventions: Evidence from a Field Experiment — Arxiv
- Impact of AI Product Recommendations on Online Purchase Intent — Daily Brew
- Boundedly Rational Meta-Learning in Sequential Consumer Choice — Arxiv