Itinerary-aware Personalized Deep Matching at Fliggy
This work improves recommendation accuracy for users on a specific online travel platform, but it is incremental as it builds on existing deep matching networks with domain-specific adaptations.
The paper tackles the problem of matching travel items for users on an online travel platform by addressing challenges of sparsity, diversity, and implicitness, resulting in superior performance in offline and online tests compared to state-of-the-art methods.
Matching items for a user from a travel item pool of large cardinality have been the most important technology for increasing the business at Fliggy, one of the most popular online travel platforms (OTPs) in China. There are three major challenges facing OTPs: sparsity, diversity, and implicitness. In this paper, we present a novel Fliggy ITinerary-aware deep matching NETwork (FitNET) to address these three challenges. FitNET is designed based on the popular deep matching network, which has been successfully employed in many industrial recommendation systems, due to its effectiveness. The concept itinerary is firstly proposed under the context of recommendation systems for OTPs, which is defined as the list of unconsumed orders of a user. All orders in a user itinerary are learned as a whole, based on which the implicit travel intention of each user can be more accurately inferred. To alleviate the sparsity problem, users' profiles are incorporated into FitNET. Meanwhile, a series of itinerary-aware attention mechanisms that capture the vital interactions between user's itinerary and other input categories are carefully designed. These mechanisms are very helpful in inferring a user's travel intention or preference, and handling the diversity in a user's need. Further, two training objectives, i.e., prediction accuracy of user's travel intention and prediction accuracy of user's click behavior, are utilized by FitNET, so that these two objectives can be optimized simultaneously. An offline experiment on Fliggy production dataset with over 0.27 million users and 1.55 million travel items, and an online A/B test both show that FitNET effectively learns users' travel intentions, preferences, and diverse needs, based on their itineraries and gains superior performance compared with state-of-the-art methods. FitNET now has been successfully deployed at Fliggy, serving major online traffic.