Online Travel Platforms (OTPs) suffer significant revenue loss from supply strikes, where rooms with physical vacancies appear sold out due to delays in manual inventory updates from hotels. While proactively adding inventory is a potential solution, this intervention faces a dual risk: hotels may later reject the booking, and more critically, the intervention might not generate platform-wide revenue, but merely shift sales from a competing hotel. This paper is the first to formalize the inventory decision on OTPs as a causal inference problem. We propose CS2NET, a Causality-Driven, Scarcity- and Service-Aware Network that estimates the platform-wide Individual Treatment Effect of each inventory addition. CS2NET addresses the unique challenges of the OTP environment by integrating: (1) a Room Type Scarcity Representation module for inferring true room availability, (2) a Hotel Service-Engagement Representation module for predicting hotel acceptance, and (3) a bias-corrected causal framework to estimate platform-level uplift while mitigating selection bias. Extensive experiments and an online A/B test on a major OTP, demonstrate that CS2NET significantly increases confirmed bookings and platform revenue, generating over 10 million RMB in additional annual GMV. We also release the first causality dataset for third-party inventory management.

Online Travel Platforms (OTPs) serve as crucial bridges between hotels and users, hotel staff can synchronize room inventory information with OTPs through manual and auto modes. In the manual mode, the hotel staff must manually maintain the inventory information on the OTPs. This mode often leads to the "inventory synchronization delay'' phenomenon where OTPs show no availability while hotels still have available rooms, seriously affecting the competitiveness of OTPs and hotel sales. To address this issue, Fliggy uses inventory add prediction (IAP) to determine whether to add an inventory for the sold-out room type. However, in practice, accurate modeling of IAP faces significant challenges due to the data sparsity. In this paper, we propose a Contrastive Learning framework for Inventory Add Prediction at Fliggy (CL4IAP), which consists of the Joint Pay-Accept Prediction Module, the Data Augmentation Module, and the Contrastive Learning Module. Specifically, the Joint Pay-Accept Prediction Module aims to predict the likelihood of generating an order and the hotel acceptance after adding an inventory. It also includes a specially designed correlation enhancement component that facilitates the expert prediction network's learning through knowledge transfer based on inter-task correlation. In the Data Augmentation Module, we design three novel data augmentation strategies for the first time based on the correlation and importance of features. In the Contrastive Learning Module, we design instance-level and cluster-level contrastive losses, which aim to minimize the distance between positive sample pairs and mitigate the negative impact of false negative sample pairs, respectively. Both offline and online experiments demonstrate the effectiveness of CL4IAP, and CL4IAP has been successfully deployed on Fliggy.

Cognitive diagnosis is a fundamental issue of intelligent education platforms, whose goal is to reveal the mastery of students on knowledge concepts. Recently, certain efforts have been made to improve the diagnosis precision, by designing deep neural networks-based diagnostic functions or incorporating more rich context features to enhance the representation of students and exercises. However, how to interpretably infer the student's mastery over non-interactive knowledge concepts (i.e., knowledge concepts not related to his/her exercising records) still remains challenging, especially when not giving relations between knowledge concepts. To this end, we propose a Knowledge-Sensed Cognitive Diagnosis (KSCD) framework, aiming at learning intrinsic relations among knowledge concepts from student response logs and incorporating them for inferring students' mastery over all knowledge concepts in an end-to-end manner. Specifically, we firstly project students, exercises and knowledge concepts into embedding representation matrices, where the intrinsic relations among knowledge concepts are reflected in the knowledge embedding representation matrix. Then, the knowledge-sensed student knowledge mastery vector and exercise factor vectors are obtained by the multiply product of their embedding representations and the knowledge embedding representation matrix, which make the student's mastery of non-interactive knowledge concepts be interpretably inferred. Finally, we can utilize classical student-exercise interaction functions to predict student's exercising performance and jointly train the model. In additional, we also design a new function to better model the student-exercise interactions. Extensive experimental results on two real-world datasets clearly show the significant performance gain of our KSCD framework, especially in predicting students' mastery over non-interactive knowledge concepts, by comparing to state-of-the-art cognitive diagnosis models (CDMs).