With the flourishing development of aviation and the convenience of booking flights online, nearby flight recommendation has become the core business of Online Travel Platforms (OTPs). Nearby flight addresses the issue of inadequate flight options for travelers by offering more cost-effective alternatives, such as recommending flights from nearby cities or on nearby departure dates. Currently, mainstream OTPs adopt rule-based or simple user preference-based strategies to recommend nearby flights. However, the insufficient emphasis on the user's historical behaviors and the ignorance of nearby flight's context make these existing strategies less effective in solving the nearby flight recommendation. To this end, a Context-based Personalized Deep Net work (CPNet) is proposed in this paper for nearby flight recommendation. In CPNet, a Personalized Preferences Learning (PPL) component is first proposed to encapsulate users' individual preferences, leveraging crucial feature correlations between historical behaviors and target nearby flight. Then, a Historical Cost Learning (HCL) component is designed to learn the price sensitivity of users under the same query and the same nearby flight recommendation. Finally, we present a Context Potential Gain Learning (CPGL) component, where the important cost between target nearby flight and context flights are emphasized and learned. Offline experiments on a production dataset and a world-scale online A/B test at Fliggy. Fliggy: https://www.fliggy.com/ both demonstrate the superiority of the proposed CPNet over baselines. CPNet is now successfully deployed at Fliggy, one of the largest OTPs in China, serving millions of users every day for flight reservations.

Broad adoption of Online Travel Platforms (OTPs) has led to increasing interest in accurately predicting users' hotel purchase behavior, with price being a key influencer in user decision-making and receiving significant focus. In examining the hotel purchasing process, we identify a pervasive trend that users make extensive price comparisons before making decisions. Existing research primarily focuses on a hotel's own price, neglecting the complex dynamics of market-driven price competition. In this paper, we propose the concept of Marketplace-oriented Hotel Price Competitiveness (MHPC) to model a hotel's pricing competitiveness within the marketplace. Being independent of specific user preferences, MHPC can be applied to and improve various downstream operations in the online hotel industry, such as hotel ranking and pricing, ultimately benefiting hoteliers, users, and OTPs. Furthermore, a novel Hotel Price Competitiveness-aware Purchase Prediction Model (HP3M) is constructed by incorporating MHPC and demand dynamics into a multi-task learning framework, featuring three distinct submodules to encompass the tri-dimensional facets of MHPC. Extensive offline and online experiments demonstrate HP3M's effectiveness in predicting hotel purchase probability and enhancing the performance of hotel ranking and pricing compared to the state-of-the-art methods. HP3M has been fully deployed on Fliggy, a leading OTP in China, serving thousands of hoteliers and tens of millions of users.

Personalized product search provides significant benefits to e-commerce platforms by extracting more accurate user preferences from historical behaviors. Previous studies largely focused on the user factors when personalizing the search query, while ignoring the item perspective, which leads to the following two challenges that we summarize in this paper: First, previous approaches relying only on co-occurrence frequency tend to overestimate the conversion rates for popular items and underestimate those for long-tail items, resulting in inaccurate item similarities; Second, user purchasing propensity is highly heterogeneous according to the popularity of the target item: it is less correlated with the user's historical behavior for a popular item and more correlated for a long-tail item. To address these challenges, in this paper we propose NAM, a Normalization Attention Model, which optimizes ''when to personalize'' by utilizing Inverse Item Frequency (IIF) and employing a gating mechanism, as well as optimizes ''how to personalize'' by normalizing the attention mechanism from a global perspective. Through comprehensive experiments, we demonstrate that our proposed NAM model significantly outperforms state-of-the-art baseline models. Furthermore, we conducted an online A/B test at Fliggy, and obtained a significant improvement of 0.8% over the latest production system in conversion rate.

Graph neural networks (GNNs) are prone to catastrophic forgetting of past experience in continuous learning scenarios. In this work, we propose a novel method for class-incremental graph learning (CGL) by class prototype construction and augmentation, which can effectively overcome catastrophic forgetting and requires no storage of exemplars (i.e., data-free). Concretely, on the one hand, we construct class prototypes in the embedding space that contain rich topological information of nodes or graphs to represent past data, which are then used for future learning. On the other hand, to boost the adaptability of the model to new classes, we employ class prototype augmentation (PA) to create virtual classes by combining current prototypes. Theoretically, we show that PA can promote the model's adaptation to new data and reduce the inconsistency of old prototypes in the embedding space, therefore further mitigate catastrophic forgetting. Extensive experiments on both node and graph classification datasets show that our method significantly outperforms the existing methods in reducing catastrophic forgetting, and beats the existing methods in most cases in terms of classification accuracy.

Broad adoption of online travel platforms (OTPs) has led to increasing focus on hotel dynamic pricing algorithms, which directly affect the revenue of platform and hotels. Existing approaches, which directly model the correlation between price and occupancy, have limitations in improving occupancy prediction accuracy while ensuring interpretability for dynamic pricing. Moreover, these methods struggle to address the significant data sparsity issue in hotel pricing scenarios. To overcome these limitations, we propose a novel Causality-driven Hotel Dynamic Pricing Model (CANDY) that captures the essential causal relationship between price and occupancy, enhancing occupancy prediction accuracy and interpretability for dynamic pricing. Specifically, we decompose confounders into three orthogonal groups of factors: characteristic factors, competitive factors, and temporal factors, and design submodules to capture the features of each dimension. To address the treatment bias and sample imbalance issues faced by existing causal inference methods in hotel pricing scenarios, we propose a novel data augmentation method based on the monotonic relationship between price and occupancy, and further design a multi-task learning framework tailored to multi-valued treatment scenarios, simultaneously alleviating the data sparsity issue. Both offline and online experiments demonstrate the effectiveness of CANDY in occupancy prediction and dynamic pricing. CANDY has been successfully deployed to provide price suggestion service at Fliggy, a leading OTP in China, serving thousands of hotel operators.

With the popularity of online shopping in recent years, various prohibited items are continuously attacking e-commerce portals. Searching and deleting such risk items online has played a fundamental role in protecting the health of e-commerce trades. To mitigate negative impact of limited supervision and adversarial behaviors of malicious sellers, current state-of-the-art work mainly introduces heterogeneous graph neural network with further improvements such as graph structure learning, pairwise training mechanism, etc. However, performance of these models is highly limited since domain knowledge is indispensable for identifying prohibited items but ignored by these methods. In this paper, we propose a novel Knowledge Based Prohibited item Detection system (named KBPD) to break through this limitation. To make full use of rich risk knowledge, the proposed method introduces the Risk-Domain Knowledge Graph (named RDKG), which is encoded by a path-based graph neural network method. Furthermore, to utilize information from both the RDKG and the Heterogeneous Risk Graph (named HRG), an interactive fusion framework is proposed and further improves the detection performance. We collect real-world datasets from the largest Chinese second-hand commodity trading platform, Xianyu. Both offline and online experimental results consistently demonstrate that KBPD outperforms the state-of-the-art baselines. The improvement over the second-best method is up to 22.67% in the AP metric.

Reviews on online shopping websites affect the buying decisions of customers, meanwhile, attract lots of spammers aiming at misleading buyers. Xianyu, the largest second-hand goods app in China, suffering from spam reviews. The anti-spam system of Xianyu faces two major challenges: scalability of the data and adversarial actions taken by spammers. In this paper, we present our technical solutions to address these challenges. We propose a large-scale anti-spam method based on graph convolutional networks (GCN) for detecting spam advertisements at Xianyu, named GCN-based Anti-Spam (GAS) model. In this model, a heterogeneous graph and a homogeneous graph are integrated to capture the local context and global context of a comment. Offline experiments show that the proposed method is superior to our baseline model in which the information of reviews, features of users and items being reviewed are utilized. Furthermore, we deploy our system to process million-scale data daily at Xianyu. The online performance also demonstrates the effectiveness of the proposed method.