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.

Click-through rate (CTR) prediction is a crucial task in many applications (e.g. recommender systems). Recently deep learning based models have been proposed and successfully applied for CTR prediction by focusing on feature interaction or user interest based on the item-to-item relevance between user behaviors and candidate item. However, these existing models neglect the user-to-user relevance between the target user and those who like the candidate item, which can reflect the preference of target user. To this end, in this paper, we propose a novel Deep User Match Network (DUMN) which measures the user-to-user relevance for CTR prediction. Specifically, in DUMN, we design a User Representation Layer to learn a unified user representation which contains user latent interest based on user behaviors. Then, User Match Layer is designed to measure the user-to-user relevance by matching the target user and those who have interacted with candidate item and modeling their similarities in user representation space. Extensive experimental results on three public real-world datasets validate the effectiveness of DUMN compared with state-of-the-art methods.

Flight itinerary ranking is critical for Online Travel Agencies (OTAs) since more and more customers book flights online. Currently, most OTAs still adopt rule-based strategies. However, rule-based methods are not able to model context-aware information and user preferences. To this end, a novel Personalized Flight itinerary Ranking Network (PFRN) is proposed in this paper. In PFRN, a Listwise Feature Encoding (LFE) structure is proposed to capture global context-aware information and mutual influences among inputs. Besides, we utilize behaviors of both individual user and group users sharing the same intention to express user preferences. Then a User Attention Mechanism is proposed to rank flight itineraries based on the user preferences effectively and efficiently. Offline experiments on real-world datasets from Amadeus and Fliggy show the superior performance of the proposed PFRN. Moreover, PFRN has been successfully deployed on online system for searching itineraries at Fliggy and achieved significant improvements.

The main task of personalized recommendation is capturing users' interests based on their historical behaviors. Most of recent advances in recommender systems mainly focus on modeling users' preferences accurately using deep learning based approaches. There are two important properties of users' interests, one is that users' interests are dynamic and evolve over time, the other is that users' interests have different resolutions, or temporal-ranges to be precise, such as long-term and short-term preferences. Existing approaches either use Recurrent Neural Networks (RNNs) to address the drifts in users' interests without considering different temporal-ranges, or design two different networks to model long-term and short-term preferences separately. This paper presents a multi-resolution interest fusion model (MRIF) that takes both properties of users' interests into consideration. The proposed model is capable to capture the dynamic changes in users' interests at different temporal-ranges, and provides an effective way to combine a group of multi-resolution user interests to make predictions. Experiments show that our method outperforms state-of-the-art recommendation methods consistently.