With the development of generative models, dialogue systems play an important role in many web applications, such as E-commerce and Question-Answering websites. The accurate user satisfaction estimation (USE) is a critical problem in measuring the quality of dialogue systems. In e-commerce, users usually seek consultation through dialogue systems to know detailed information about the products they intend to purchase. Existing studies mainly focus on analyzing user sentiment in a dialogue for USE, neglecting to understand what the user is concerned about when requesting a consultation. It may cause fatal errors when the response is emotionally friendly but non-informative. Thus, to evaluate how a dialogue satisfies the user's requirements, it is essential to have a conceptual understanding of the products to determine if the response has addressed the user's question. In this paper, we propose a knowledge-enhanced USE model named CoRe-USE, which introduces the Conceptual Knowledge Reasoning for USE in E-Commerce Dialogue Systems. We first design a simple yet efficient entity linking and relation selection module enabling conceptual reasoning in each dialogue. Then, we propose a hierarchical encoder to capture the contextual information in multi-turn dialogues. Finally, we introduce a knowledge enhancement module to fuse conceptual reasoning into contextual embeddings to produce USE. For evaluation, we conduct experiments on three real-world datasets in various scenarios, the results demonstrate the effectiveness and robustness of CoRe-USE compared with SOTA baselines.

In logistic systems, demand prediction is an essential task providing the basis for improving the quality of terminal services, such as pick-up and delivery efficiency. However, the geographical scope of operations across multiple cities brings challenges due to the sparsity of user behavior data, hindering accurate predictions. Despite cross-city prediction methods potentially solving this problem by relying on the label of overlapping users in different cities, annotating these overlapping users is expensive. Additionally, the dynamic and diverse nature of user behaviors complicates feature transfer between cities. In this work, we define the logistics demand prediction problem as forecasting pick-up and delivery demand for zones, the smallest operational units in logistics systems, in different cities. To address the challenge, we propose TSTL, a Transferable Spatio-Temporal Learning framework for cross-city logistics prediction with sparse user data. TSTL advances existing methods from two aspects: (1) User-level invariant representation module extracts consistent user representations for overlapping and non-overlapping users across cities. (2) User-zone graph aggregation module enhances user embeddings by integrating dynamic interactions, such as logistics behaviors, into inherent user relations. Finally, the multi-city transfer module fine-tunes model parameters for city-invariant knowledge adoption and predicts future logistics demand. We implement and evaluate TSTL on one of the largest logistics systems. Extensive offline experiments and real-world deployment demonstrate the effectiveness of TSTL.

Spatio-temporal prediction focuses on jointly modeling spatial correlations and temporal evolution and has a wide range of applications. Due to the heterogeneity of spatio-temporal data, accurate prediction relies on effectively integrating topological structures and sequential patterns. Although recurrent graph learning methods excel at capturing dynamic graph patterns, explicitly inferring future snapshots from historical dynamic graphs remains a significant challenge. Recently, prompt-based graph learning has shown the potential to improve future snapshot inference by leveraging node or task-specific prompts. However, these methods fail to fully capture edge information resulting in incomplete and less accurate representations of future snapshot structures. To bridge this gap, we propose ProST, a framework that Prompts future snapshots on dynamic graphs for Spatio-Temporal prediction, which leverages dynamic graph pre-training to generate a premise graph containing historical graph information and then employs prompts on the premise graph to infer explicit future snapshots. Specifically, this framework comprises three steps: Firstly, dynamic graph pre-training is performed using multi-granularity evolution graph convolution to obtain the premise graph with both local and global features of dynamic graphs. Secondly, prompt subgraphs are used to prompt node pairs and edge features within the premise graph. The subgraph prompt aggregation mechanism propagates this information to generate future snapshots. Finally, we freeze the parameters of the pre-trained model and update the subgraph prompt parameters using meta-learning to adapt to downstream spatio-temporal prediction tasks. Extensive experiments on real-world datasets validate that ProST achieves state-of-the-art performance.

With the development of the logistics industry, the user base of logistics services has expanded swiftly. This rapid increase in user scale presents significant challenges for logistics business management. A fundamental issue in such scenarios is audience expansion, which aims to find users willing to sign long-term services with logistics companies to foster business growth. Existing methods in addressing audience expansion mainly assume user modeling is entangled and neglects the inherent community structure among users. Due to these limitations, the effectiveness of traditional methods in achieving accurate user expansion is often restricted. Our work introduces a novel heterogeneous graph-based model, named Hi-DGN, which concentrates on the Hierarchical information propagation and aggregation in Disentangled Graph Networks for audience expansion. It consists of three main components: (i) the disentangled embedding layer to decouple user representations into different aspects, enabling the extraction of differentiated features; (ii) the hierarchical information propagation module partitions individual nodes into distinct groups and propagates information from group nodes to individual nodes hierarchically to capture diverse granularity representations; and (iii) the aggregation module to fuse all relation-specific embeddings to generate global node embeddings. Extensive experiments on two real-world datasets demonstrate the effectiveness of our method in various evaluation settings.

Integrated Warehousing and Distribution Supply Networks (IWDSN) have shown their high efficiency in E-commerce. Efficient supply capacity prediction is crucial for logistics systems to maintain the delivery capacity to meet users' requirements. However, unforeseen events such as extreme weather and public health emergencies pose challenges in supply forecasting. Previous work mainly infers supply optimization based on the invariant topology of logistic networks, neglecting dynamic routing and distinct node effects reacting to emergencies. To address these challenges, the hierarchical relations among warehouses, sorting centers, and delivery stations in logistic networks are necessary to learn the diverse reactions. In this paper, we propose a hierarchical spatio-temporal graph learning model to predict the emergency supply capacity of IWDSN based on micro and macro graphs. The micro graph shows transportation connectivity while the macro graph shows the geographical correlation. Specifically, it consists of three components. (1) For micro graphs, a metapath aggregation strategy is designed to capture dynamic routing information on both route-view and event-view graphs. (2) For macro graphs, a bipartite graph learning approach to extract spatial representations. (3) For spatio-temporal feature fusion, the spatio-temporal joint forecasting module combines the temporal feature from the time-series encoder with hierarchical spatial features to predict the future supply capacity. The extensive experiments on two real-world datasets demonstrate the effectiveness of our proposed model, which achieves state-of-the-art performance compared with advanced baselines.

In recent years, emergency last-mile logistics (ELML) have played an essential role in urban emergencies. The efficient allocation of couriers in ELML is of practical significance to ensure the supply of essential materials, especially in public health emergencies (PHEs). However, couriers allocation becomes challenging due to the instability of demand, dynamic supply comprehension, and the evolutional delivery environment for ELML caused by PHEs. While existing work has delved into couriers allocation, the impact of PHEs on demand-supply-delivery has yet to be considered. In this work, we design PTOCA, a Predict-Then-Optimize Couriers Allocation framework. Specifically, in the prediction stage, we design a resource-aware prediction module that performs spatio-temporal modeling of unstable demand characteristics using a variational graph GRU encoder and builds a task-resource regressor to predict demand accurately. In the optimization stage, firstly, the priority ranking module solves the matching of delivery resources under demand-supply imbalance. Then the multi-factor task allocation module is used to model the dynamic evolutional environment and reasonably assign the delivery tasks of couriers. We evaluate PTOCA using real-world data covering 170 delivery zones, more than 10,000 couriers, and 100 million delivery tasks. The data is collected from JD Logistics, one of the largest logistics service companies. Extensive experimental results show that our method outperforms the baseline in task delivery rate and on-time delivery rate.