  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.
