xisting online advertising systems generate high-quality ad recommendations through a complex online serving pipeline whenever a user's ad request arrives. However, our analyses on the display advertising system in Taobao show that this paradigm could lead to an inefficient utilization of computational resources. In this work, we propose an intelligent computation recycling framework called ComRecycle, which caches and reuses unexposed ad sets for repetitive ad requests to improve the computational resource utilization. We introduce fine-grained computation recycling strategies, and formulate the computation recycling decision as an online constrained optimization problem. Therefore, ComRecycle can achieve the goal of reducing computation costs while guaranteeing the same level of recommendation performance. Extensive offline experiments validate the correctness and effectiveness of ComRecycle. Our online A/B testing demonstrates that ComRecycle can save over 20% computational resources while maintaining the same system performance as the baseline.

Online advertising has become a core revenue driver for internet industry, with ad auctions playing a crucial role in ensuring platform revenue and advertiser incentives. Classical auction mechanisms, such as GSP, rely on the independent CTR assumption and fail to account for the interplay among the displayed items, also called as externalities in economics. Recent advancements in learning-based auctions enable the encoding of high-dimensional contextual features. However, existing methods are limited by the ''prediction-before-allocation'' design paradigm, which models set-level externalities within candidate ads and fails to consider the context of the final allocation, leading to suboptimal results. In this work, we introduce Contextual Generative Auction (CGA), a novel framework that incorporates permutation-level externalities in multi-slot ad auctions. Built on the structure of our theoretically derived optimal auction, CGA decouples the optimization of allocation and payment. We construct an autoregressive generative model for allocation, and reformulate incentive compatibility (IC) constraint into minimizing ex-post regret that supports gradient computation, enabling end-to-end learning of the optimal payment rule. Extensive offline and online experiments demonstrate that CGA significantly enhances platform revenue and CTR compared to existing methods, and effectively approximates the optimal auction with nearly maximal revenue and minimal regret.

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.

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.

Motivated by the collaboration with Fliggy1, a leading Online Travel Platform (OTP), we investigate an important but less explored research topic about optimizing the quality of hotel supply, namely selecting potential profitable hotels in advance to build up adequate room inventory. We formulate a WWW problem, i.e., within a specific time period (When) and potential travel area (Where), which hotels should be recommended to a certain group of users with similar travel intentions (Why). We identify three critical challenges in solving the WWW problem: user groups generation, travel data sparsity and utilization of hotel recommendation information (e.g., period, location and intention). To this end, we propose LINet, a Location and Intention-aware neural Network for hotel group recommendation. Specifically, LINet first identifies user travel intentions for user groups generalization, and then characterizes the group preferences by jointly considering historical user-hotel interaction and spatio-temporal features of hotels. For data sparsity, we develop a graph neural network, which employs long-term data, and further design an auxiliary loss function of location that efficiently exploits data within the same and across different locations. Both offline and online experiments demonstrate the effectiveness of LINet when compared with state-of-the-art methods. LINet has been successfully deployed on Fliggy to retrieve high quality hotels for business development, serving hundreds of hotel operation scenarios and thousands of hotel operators.