Deep Inclusion Relation-aware Network for User Response Prediction at Fliggy
Zai Huang,Mingyuan Tao,Bufeng Zhang
User response prediction plays a crucial role in many applications (e.g. search ranking and personalized recommendation) at online travel platforms. Although existing methods have made a great success by focusing on feature interaction or user behaviors, they cannot synthetically exploit item inclusion relations describing relationships of an item including or being included by another one, which are important components among travel items. To this end, in this paper, we propose a novel Deep Inclusion Relation-aware Network (DIRN) for user response prediction by synthetically exploiting inclusion relations among travel items. Specifically, on the item graph constructed with inclusion relations, we first leverage a node embedding approach to learn the item graph-based embedding. Then, we design Representation-based Interest Layer and Relation Path Interest Layer to extract user latent interest with user behaviors in two ways. Representation-based Interest Layer models the item-to-item similarity based on item representations containing the graph-based embedding with an attention mechanism and obtains user temporal interest by summing up representations of interacted items with similarities. Relation Path Interest Layer measures item-to-item realistic associations to extract user interest with inclusion relation paths. Offline experiments on a real-world data from Fliggy clearly validate the effectiveness of DIRN. Furthermore, DIRN has been successfully deployed online in search ranking at Fliggy and achieves significant improvement.
