Auto-bidding is crucial in facilitating online advertising by automatically providing bids for advertisers. While previous work has made great efforts to model bidding environments for better ad performance, it has limitations in generalizability across environments since these models are typically tailored for specific bidding scenarios. To this end, we approach the scenario-independent principles through a unified function that estimates the achieved effect under specific bids, such as budget consumption, gross merchandise volume (GMV), page views, etc. Then, we propose a bidding foundation model Bid2X to learn this fundamental function from data in various scenarios. Our Bid2X is built over uniform series embeddings that encode heterogeneous data through tailored embedding methods. To capture complex inter-variable and dynamic temporal dependencies in bidding data, we propose two attention mechanisms separately treating embeddings of different variables and embeddings at different times as attention tokens for representation learning. On top of the learned variable and temporal representations, a variable-aware fusion module is used to perform adaptive bidding outcome prediction. To model the unique bidding data distribution, we devise a zero-inflated projection module to incorporate the estimated non-zero probability into its value prediction, which makes up a joint optimization objective containing classification and regression. The objective is proven to converge to the zero-inflated distribution.

In the rapidly evolving field of e-commerce, the effectiveness of search re-ranking models is crucial for enhancing user experience and driving conversion rates. Despite significant advancements in feature representation and model architecture, the integration of multimodal information remains underexplored. This study addresses this gap by investigating the computation and fusion of textual and visual information in the context of re-ranking. We propose Advancing Re-ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks (ARMMT), which integrates an attention-based multimodal fusion technique and an auxiliary ranking-aligned task to enhance item representation and improve targeting capabilities. This method not only enriches the understanding of product attributes but also enables more precise and personalized recommendations. Experimental evaluations on JD.com's search platform demonstrate that ARMMT achieves state-of-the-art performance in multimodal information integration, evidenced by a 0.22% increase in the Conversion Rate (CVR), significantly contributing to Gross Merchandise Volume (GMV). This pioneering approach has the potential to revolutionize e-commerce re-ranking, leading to elevated user satisfaction and business growth.

Traffic flow prediction is a fundamental problem in spatiotemporal data mining. Most of the existing studies focuses on designing statistical models to fit historical traffic data, which are purely data-driven approaches and fail to reveal the underlying mechanisms of urban traffic. To address this issue, we propose the spatiotemporal potential energy field model (ST-PEF+), which applies the field theory for human mobility to interpret the underlying mechanisms of urban traffic, and introduces the theory into data-driven deep learning models. ST-PEF+ consists of a PEF extraction module and a data-driven module. Inspired by the field theory for human mobility, the PEF extraction module adopts an algorithm to decompose the grid-based traffic flow graph into several polytree-based potential energy fields (PEFs), where traffic flows from high potential locations to low potential locations, just as water is driven by the gravity field. We also provide a theoretical analysis to ensure that the polytree decomposition algorithm can decompose any traffic flow graph. In the data-driven module, ST-PEF+ learns a spatiotemporal deep learning model to predict the dynamics of PEFs. The model adopts correlation-adaptive neural network structures, which consists of a temporal component for temporal correlations and a spatial component for spatial correlations. The temporal component employs a GRU and DCN combined structure to capture both short-term autocorrelation and long-term repeating patterns of PEFs. The spatial component extends the GAT using weighted directed attention to model the asymmetric spatial structure in PEFs. The prediction results of traffic flow are finally derived from PEFs that are predicted by the spatiotemporal deep learning model. We conduct extensive evaluations on three real-world traffic datasets. The results show that our model outperforms the state-of-the-art baselines. In addition, case studies confirm that the PEFs learned in our framework can reveal the underlying mechanisms of urban traffic, thus improving the model interpretability.

With the unprecedented development of industrialization and urbanization, many hazardous chemicals have become an indispensable part of our daily life. They are produced, transported, and consumed in modern cities every day, which breeds many unknown hazardous chemicals-related locations (HCLs) that are out of the supervision of management departments and accompanying huge threats to urban safety. How to recognize these unknown HCLs and identify their risk levels is an essential task for urban hazardous chemicals management. To accomplish this task, in this work, we propose a system named as CityShield to discover hidden HCLs and classify their risk levels based on trajectories of hazardous chemicals transportation vehicles. The CityShield system consists of three components. The first component is Data Pre-processing, which filters noises in raw trajectories and probes stable transportation vehicles' stay points from massive uncertain GPS points. The second is HCL Recognition, which adopts the proposed HCL-Rec algorithm to cluster stay points into polygonal HCLs, and avoids the improper location merging problem caused by the skewed spatial distribution of HCLs. The third component is HCL Classification, which introduces the HCL relation graph as auxiliary information to overcome the label scarcity problem of HCLs. It adopts a self-supervised method consisting of four pre-training tasks to learn high-quality representations for HCLs from the graph, which are finally used to classify the categories and risk levels of HCLs.

Traffic flow prediction is of great importance in traffic management and public safety, but is challenging due to the complex spatial-temporal dependencies as well as temporal dynamics. Existing work either focuses on traditional statistical models, which have limited prediction accuracy, or relies on black-box deep learning models, which have superior prediction accuracy but are hard to interpret. In contrast, we propose a novel interpretable spatiotemporal deep learning model for traffic flow prediction. Our main idea is to model the physics of traffic flow through a number of latent Spatio-Temporal Potential Energy Fields (ST-PEFs), similar to water flow driven by the gravity field. We develop a Wind field Decomposition (WD) algorithm to decompose traffic flow into poly-tree components so that ST-PEFs can be established. We then design a spatiotemporal deep learning model for the ST-PEFs, which consists of a temporal component (modeling the temporal correlation) and a spatial component (modeling the spatial dependencies). To the best of our knowledge, this is the first work that make traffic flow prediction based on ST-PEFs. Experimental results on real-world traffic datasets show the effectiveness of our model compared to the existing methods. A case study confirms our model interpretability.