Spatial crowdsourcing platforms have become indispensable in addressing the evolving needs of modern society. These platforms facilitate essential services such as ride-sharing, on-demand food delivery, and efficient parcel distribution. However, the uneven distribution of workers and requests under a single-platform setting may lead to the loss of tasks. To address this issue, we introduce the Cross Online Matching (COM) problem, which facilitates collaboration among multiple platforms. We first propose DemCOM and RamCOM, which adopt deterministic greedy and randomized trade-off strategies, respectively. Furthermore, we develop a Utility-Distribution Aware Cooperative Online Matching (UDACOM) algorithm that leverages supply-demand relationships to optimize decision-making. Theoretical analysis confirms the competitive ratios of our algorithms. Validated on both real and synthetic datasets, our approach significantly outperforms state-of-the-art methods, achieving a 5% increase in total revenue and a 3% improvement in the successful matching rate.

Abstract In recent years, the task of knowledge graph completion has attracted significant attention from researchers. In practical scenarios, multi-source knowledge graph completion is quite common. Federated knowledge graph embedding enables joint learning across multiple knowledge graphs while ensuring data privacy and security. Generally, each data source has a different data distribution. They may exhibit various connections, such as combinatorial, hierarchical, and symmetric/asymmetric connections. Existing federated knowledge graph models overlook the data heterogeneity of knowledge graphs from different sources, using a unified scoring function to assess the quality of the generated embedding vectors from different clients. This limitation affects the quality of knowledge graph embeddings generated by each client. Therefore, this paper proposes a federated knowledge graph embedding framework based on the diffusion model. On the client side, we employ diffusion model to learn knowledge graph embeddings. We utilize the diffusion model's forward noise-adding process to learn the knowledge graph's distribution. We then use the reverse denoising process to generate knowledge embeddings directly. Additionally, we employ knowledge distillation during client model training to address the drift between local optimization and global convergence. Since the original data cannot leave the local environment in federated learning, we adopt a framework that shares diffusion models for federated knowledge graph completion. Extensive experiments demonstrate that our model significantly outperforms existing state-of-the-art methods in three benchmark datasets.

The last few years have seen the wide applications of ride-sharing, a transportation service that allows users to share their travel routes. A typical problem for ride-sharing is to find an optimal route for each worker to serve the dynamically arriving requests with different objectives. Previous studies focus on the route planning on a single platform. However, a single platform may have an uneven distribution of supply and demand, which causes the platform to lose requests from lack of available workers. Luckily, some ride-sharing platforms provide the same service, which enables their collaborations. The inter-platform collaborations on ride-sharing can ease the worker shortages and greatly improve the service quality, but have not been studied yet. In this paper, we propose a Cross Online Ride-sharing (CORS) problem, which allows a platform to borrow the available workers from other platforms to serve its own requests. We first design two algorithms to select the optimal available worker from other platforms, ROWS and DOWS. ROWS randomly picks an available worker, while DOWS selects the optimal worker with the minimum additional travel distance calculated based on his/er predicted destination direction. Then, we design an efficient CORS framework that embeds the proposed optimal worker selection algorithms for the CORS problem. Extensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of our algorithms.

The rapid development of the spatial crowdsourcing platform in the fields of express delivery, food delivery, and intelligent transportation has attracted widespread attention. As a typical problem in spatial crowdsourcing, online task matching problem has been widely studied. Most of the existing researches are based on the task allocation of different optimizations under one single platform. Recently, in order to solve the situation of non-uniform distribution of tasks and crowd workers on a single platform, cross online task assignment has been proposed aiming at increasing the mutual benefit through cooperations. However, existing methods lead to the situation where the local platform lends workers to other platforms, resulting in a lack of workers of itself. In this paper, we propose a Batch-Based Cooperative Task Assignment(BCTA) problem, which enables multi-platform task assignment to be completed within a tolerant time. We design a BCTA model and propose fixed-t BCTA(FT-BCTA) algorithm and adaptive BCTA(Adt-BCTA) algorithm to solve the BCTA problem. FT-BCTA focuses on a fixed batching strategy, while Adt-BCTA considers the batching strategy adaptively according to the supply and demand of multi-platforms. Extensive experiments on both real datasets and synthetic datasets show the effectiveness and efficiency of our algorithms.

Spatial platforms have become increasingly important in people's daily lives. Task assignment is a critical problem in these platforms that matches real-time orders to suitable workers. Most studies only focus on independent platforms that are in a competitive relationship. Recently, an emerging service model was proposed, where orders are shared with multiple similar platforms. It aims to solve the imbalance between supply and demand through cooperation. However, it faces the following main challenges: 1) Coordinating independent platforms fairly based on the limited information; 2) Building a task assignment process with personalized algorithms. In this paper, we study real applications and define the Autonomy and Coordination Task Assignment problem (ACTA) to maximize the global revenue and fairness. We propose a framework to solve ACTA that consists of public order sending, local matching, global conflict adjustment and results notification. The framework uses mid-products and public data to train a revenue estimation model to coordinate participants. We further propose dynamic weight task assignment algorithms to guarantee fairness. Through the experiments, we prove that the platforms can obtain higher revenue, which shows the effectiveness and efficiency of our work.

With the continuous development of spatial crowdsourcing platform, online task assignment problem has been widely studied as a typical problem in spatial crowdsourcing. Most of the existing studies are based on a single-platform task assignment to maximize the platform's revenue. Recently, cross online task assignment has been proposed, aiming at increasing the mutual benefit through cooperations. However, existing methods fail to consider the data privacy protection in the process of cooperation and cause the leakage of sensitive data such as the location of a request and the historical data of cooperative platforms. In this paper, we propose Privacy-preserving Cooperative Online Matching (PCOM), which protects the privacy of the users and workers on their respective platforms. We design a PCOM framework and provide theoretical proof that the framework satisfies the differential privacy property. We then propose two PCOM algorithms based on two different privacy-preserving strategies. Extensive experiments on real and synthetic datasets confirm the effectiveness and efficiency of our algorithms.