A Context based Personalized Deep Network for Nearby Flight Recommendation

With the flourishing development of aviation and the convenience of booking flights online, nearby flight recommendation has become the core business of Online Travel Platforms (OTPs). Nearby flight addresses the issue of inadequate flight options for travelers by offering more cost-effective alternatives, such as recommending flights from nearby cities or on nearby departure dates. Currently, mainstream OTPs adopt rule-based or simple user preference-based strategies to recommend nearby flights. However, the insufficient emphasis on the user's historical behaviors and the ignorance of nearby flight's context make these existing strategies less effective in solving the nearby flight recommendation. To this end, a Context-based Personalized Deep Net work (CPNet) is proposed in this paper for nearby flight recommendation. In CPNet, a Personalized Preferences Learning (PPL) component is first proposed to encapsulate users' individual preferences, leveraging crucial feature correlations between historical behaviors and target nearby flight. Then, a Historical Cost Learning (HCL) component is designed to learn the price sensitivity of users under the same query and the same nearby flight recommendation. Finally, we present a Context Potential Gain Learning (CPGL) component, where the important cost between target nearby flight and context flights are emphasized and learned. Offline experiments on a production dataset and a world-scale online A/B test at Fliggy. Fliggy: https://www.fliggy.com/ both demonstrate the superiority of the proposed CPNet over baselines. CPNet is now successfully deployed at Fliggy, one of the largest OTPs in China, serving millions of users every day for flight reservations.