OpenRaiser/Intern-Atlas huggingface dataset:
Profiling urban streets: A semi-supervised prediction model based on street view imagery and spatial topology
Authors: Meng Chen 0003, Zechen Li 0003, Weiming Huang 0001, Yongshun Gong, Yilong Yin
Venue: Conference KDD 2024
Year: 2024

With the expansion and growth of cities, profiling urban areas with the advent of multi-modal urban datasets (e.g., points-of-interest and street view imagery) has become increasingly important in urban planing and management. Particularly, street view images have gained popularity for understanding the characteristics of urban areas due to its abundant visual information and inherent correlations with human activities. In this study, we define a street segment represented by multiple street view images as the minimum spatial unit for analysis and predict its functional and socioeconomic indicators, which presents several challenges in modeling spatial distributions of images on a street and the spatial topology (adjacency) of streets. Meanwhile, Large Language Models are capable of understanding imagery data based on its extraordinary knowledge base and unveil a remarkable opportunity for profiling streets with images. In view of the challenges and opportunity, we present a semi-supervised Urban Street Profiling Model (USPM) based on street view imagery and spatial adjacency of urban streets. Specifically, given a street with multiple images, we first employ a newly designed spatial context-based contrastive learning method to generate feature vectors of images and then apply the LSTM-based fusion method to encode multiple images on a street to yield the street visual representation; we then create the descriptions of street scenes for street view images based on the SPHINX (a large language model) and produce the street textual representation; finally, we build an urban street graph based on spatial topology (adjacency) and employ a semi-supervised graph learning algorithm to further encode the street representations for prediction. We conduct thorough experiments with real-world datasets to assess the proposed USPM. The experimental results demonstrate that USPM considerably outperforms baseline methods in two urban prediction tasks.
Predicting multi-level socioeconomic indicators from structural urban imagery
Authors: Tong Li, Shiduo Xin, Yanxin Xi, Sasu Tarkoma, Pan Hui, Yong Li
Venue: Conference CIKM 2022
Year: 2022

Understanding economic development and designing government policies requires accurate and timely measurements of socioeconomic activities. In this paper, we show how to leverage city structural information and urban imagery like satellite images and street view images to accurately predict multi-level socioeconomic indicators. Our framework consists of four steps. First, we extract structural information from cities by transforming real-world street networks into city graphs (GeoStruct). Second, we design a contrastive learning-based model to refine urban image features by looking at geographic similarity between images, with images that are geographically close together having similar features (GeoCLR). Third, we propose using street segments as containers to adaptively fuse the features of multi-view urban images, including satellite images and street view images (GeoFuse). Finally, given the city graph with a street segment as a node and a neighborhood area as a subgraph, we jointly model street- and neighborhood-level socioeconomic indicator predictions as node and subgraph classification tasks. The novelty of our method is that we introduce city structure to organize multi-view urban images and model the relationships between socioeconomic indicators at different levels. We evaluate our framework on the basis of real-world datasets collected in multiple cities. Our proposed framework improves performance by over 10% when compared to state-of-the-art baselines in terms of prediction accuracy and recall.
Carpg: Cross-city knowledge transfer for traffic accident prediction via attentive region-level parameter generation
Authors: Guang Yang, Yuequn Zhang, Jinquan Hang, Xinyue Feng, Zejun Xie, Desheng Zhang, Yu Yang
Venue: Conference CIKM 2023
Year: 2023

Traffic accident prediction is a crucial problem for public safety, emergency treatment, and urban management. Existing works leverage extensive data collected from city infrastructures to achieve encouraging performance based on various machine learning techniques but cannot achieve a good performance in situations with limited data (i.e., data scarcity). Recent developments in transfer learning bring a new opportunity to solve the data scarcity problem. In this paper, we design a novel cross-city transfer learning framework named CARPG for predicting traffic accidents in data-scarce cities. We address the unique challenge of predicting traffic accidents caused by its two fundamental characteristics, i.e., spatial heterogeneity and inherent rareness, which result in the biased performance of the state-of-the-art transfer learning methods. Specifically, we build cross-city region connections by jointly learning the spatial region representations for both source and target cities with an inter-city global graph knowledge transfer process. Further, we design an efficient attention-based parameter-generating mechanism to learn region-specific traffic accident patterns, while controlling the total number of parameters. Built upon that, we ensure that only relevant patterns are transferred to each target region during the knowledge transfer process and further to be fine-tuned. We conduct extensive experiments on three real-world datasets, and the evaluation results demonstrate the superiority of our framework compared with state-of-the-art baseline models.
Recognising spontaneous facial micro-expressions
Authors: Tomas Pfister, Xiaobai Li, Guoying Zhao 0001, Matti Pietikäinen
Venue: Conference ICCV 2011
Year: 2011

Facial micro-expressions are rapid involuntary facial expressions which reveal suppressed affect. To the best knowledge of the authors, there is no previous work that successfully recognises spontaneous facial micro-expressions. In this paper we show how a temporal interpolation model together with the first comprehensive spontaneous micro-expression corpus enable us to accurately recognise these very short expressions. We designed an induced emotion suppression experiment to collect the new corpus using a high-speed camera. The system is the first to recognise spontaneous facial micro-expressions and achieves very promising results that compare favourably with the human micro-expression detection accuracy.
G-refer: Graph retrieval-augmented large language model for explainable recommendation
Authors: Yuhan Li, Xinni Zhang, Linhao Luo, Heng Chang, Yuxiang Ren, Irwin King, Jia Li
Venue: Conference WWW 2025
Year: 2025

Explainable recommendation has demonstrated significant advantages in informing users about the logic behind recommendations, thereby increasing system transparency, effectiveness, and trustworthiness. To provide personalized and interpretable explanations, existing works often combine the generation capabilities of large language models (LLMs) with collaborative filtering (CF) information. CF information extracted from the user-item interaction graph captures the user behaviors and preferences, which is crucial for providing informative explanations. However, due to the complexity of graph structure, effectively extracting the CF information from graphs still remains a challenge. Moreover, existing methods often struggle with the integration of extracted CF information with LLMs due to its implicit representation and the modality gap between graph structures and natural language explanations. To address these challenges, we propose G-Refer, a framework using Graph Retrieval-augmented large language models (LLMs) for explainable recommendation. Specifically, we first employ a hybrid graph retrieval mechanism to retrieve explicit CF signals from both structural and semantic perspectives. The retrieved CF information is explicitly formulated as human-understandable text by the proposed graph translation and accounts for the explanations generated by LLMs. To bridge the modality gap, we introduce knowledge pruning and retrieval-augmented fine-tuning to enhance the ability of LLMs to process and utilize the retrieved CF information to generate explanations. Extensive experiments show that G-Refer achieves superior performance compared with existing methods in both explainability and stability. Codes and data are available at https://github.com/Yuhan1i/G-Refer.
Learning global term weights for content-based recommender systems
Authors: Yupeng Gu, Bo Zhao, David Hardtke, Yizhou Sun
Venue: Conference WWW 2016
Year: 2016

Recommender systems typically leverage two types of signals to effectively recommend items to users: user activities and content matching between user and item profiles, and recommendation models in literature are usually categorized into collaborative filtering models, content-based models and hybrid models. In practice, when rich profiles about users and items are available, and user activities are sparse (cold-start), effective content matching signals become much more important in the relevance of the recommendation. The de-facto method to measure similarity between two pieces of text is computing the cosine similarity of the two bags of words, and each word is weighted by TF (term frequency within the document) x IDF (inverted document frequency of the word within the corpus). In general sense, TF can represent any local weighting scheme of the word within each document, and IDF can represent any global weighting scheme of the word across the corpus. In this paper, we focus on the latter, i.e., optimizing the global term weights, for a particular recommendation domain by leveraging supervised approaches. The intuition is that some frequent words (lower IDF, e.g. ``database'') can be essential and predictive for relevant recommendation, while some rare words (higher IDF, e.g. the name of a small company) could have less predictive power. Given plenty of observed activities between users and items as training data, we should be able to learn better domain-specific global term weights, which can further improve the relevance of recommendation.
Federated learning as variational inference: A scalable expectation propagation approach
Authors: Han Guo, Philip Greengard, Hongyi Wang 0001, Andrew Gelman, Yoon Kim, Eric P. Xing
Venue: Conference ICLR 2023
Year: 2023

The canonical formulation of federated learning treats it as a distributed optimization problem where the model parameters are optimized against a global loss function that decomposes across client loss functions. A recent alternative formulation instead treats federated learning as a distributed inference problem, where the goal is to infer a global posterior from partitioned client data (Al-Shedivat et al., 2021). This paper extends the inference view and describes a variational inference formulation of federated learning where the goal is to find a global variational posterior that well-approximates the true posterior. This naturally motivates an expectation propagation approach to federated learning (FedEP), where approximations to the global posterior are iteratively refined through probabilistic message-passing between the central server and the clients. We conduct an extensive empirical study across various algorithmic considerations and describe practical strategies for scaling up expectation propagation to the modern federated setting. We apply FedEP on standard federated learning benchmarks and find that it outperforms strong baselines in terms of both convergence speed and accuracy.
Fednlr: Federated learning with neuron-wise learning rates
Authors: Haozhao Wang, Peirong Zheng, Xingshuo Han, Wenchao Xu 0001, Ruixuan Li 0001, Tianwei Zhang 0004
Venue: Conference KDD 2024
Year: 2024

Federated Learning (FL) suffers from severe performance degradation due to the data heterogeneity among clients. Some existing work suggests that the fundamental reason is that data heterogeneity can cause local model drift, and therefore proposes to calibrate the direction of local updates to solve this problem. Though effective, existing methods generally take the model as a whole, which lacks a deep understanding of how the neurons within deep classification models evolve during local training to form model drift. In this paper, we bridge this gap by performing an intuitive and theoretical analysis of the activation changes of each neuron during local training. Our analysis shows that the high activation of some neurons on the samples of a certain class will be reduced during local training when these samples are not included in the client, which we call neuron drift, thus leading to the performance reduction of this class. Motivated by this, we propose a novel and simple algorithm called FedNLR, which utilizes <u>N</u>euron-wise <u>L</u>earning <u>R</u>ates during the FL local training process. The principle behind this is to enhance the learning of neurons bound to local classes on local data knowledge while reducing the decay of non-local classes knowledge stored in neurons. Experimental results demonstrate that FedNLR achieves state-of-the-art performance on federated learning with popular deep neural networks.
Efficient large-scale traffic forecasting with transformers: A spatial data management perspective
Authors: Yuchen Fang, Yuxuan Liang, Bo Hui, Zezhi Shao, Liwei Deng, Xu Liu, Xinke Jiang, Kai Zheng
Venue: Conference KDD 2025
Year: 2025

Road traffic forecasting is crucial in real-world intelligent transportation scenarios like traffic dispatching and path planning in city management and personal traveling. Spatio-temporal graph neural networks (STGNNs) stand out as the mainstream solution in this task. Nevertheless, the quadratic complexity of remarkable dynamic spatial modeling-based STGNNs has become the bottleneck over large-scale traffic data. From the spatial data management perspective, we present a novel Transformer framework called PatchSTG to efficiently and dynamically model spatial dependencies for large-scale traffic forecasting with interpretability and fidelity. Specifically, we design a novel irregular spatial patching to reduce the number of points involved in the dynamic calculation of Transformer. The irregular spatial patching first utilizes the leaf K-dimensional tree (KDTree) to recursively partition irregularly distributed traffic points into leaf nodes with a small capacity, and then merges leaf nodes belonging to the same subtree into occupancy-equaled and non-overlapped patches through padding and backtracking. Based on the patched data, depth and breadth attention are used interchangeably in the encoder to dynamically learn local and global spatial knowledge from points in a patch and points with the same index of patches. Experimental results on four real world large-scale traffic datasets show that our PatchSTG achieves train speed and memory utilization improvements up to 10x and 4x with the state-of-the-art performance.
Uncertainty quantification via spatial-temporal tweedie model for zero-inflated and long-tail travel demand prediction
Authors: Xinke Jiang, Dingyi Zhuang, Xianghui Zhang, Hao Chen, Jiayuan Luo, Xiaowei Gao
Venue: Conference CIKM 2023
Year: 2023

Understanding Origin-Destination (O-D) travel demand is crucial for transportation management. However, traditional spatial-temporal deep learning models grapple with addressing the sparse and long-tail characteristics in high-resolution O-D matrices and quantifying prediction uncertainty. This dilemma arises from the numerous zeros and over-dispersed demand patterns within these matrices, which challenge the Gaussian assumption inherent to deterministic deep learning models. To address these challenges, we propose a novel approach: the Spatial-Temporal Tweedie Graph Neural Network (STTD). The STTD introduces the Tweedie distribution as a compelling alternative to the traditional 'zero-inflated' model and leverages spatial and temporal embeddings to parameterize travel demand distributions. Our evaluations using real-world datasets highlight STTD's superiority in providing accurate predictions and precise confidence intervals, particularly in high-resolution scenarios. GitHub code is available online(https://github.com/STTDAnonymous/STTD).
Incentive-aware learning for large markets
Authors: Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Song Zuo
Venue: Conference WWW 2018
Year: 2018

In a typical learning problem, one key step is to use training data to pick one model from a collection of models that optimizes an objective function. In many multi-agent settings, the training data is generated through the actions of the agents, and the model is used to make a decision (e.g., how to sell an item) that affects the agents. An illustrative example of this is the problem of learning the reserve price in an auction. In such cases, the agents have an incentive to influence the training data (e.g., by manipulating their bids in the case of an auction) to game the system and achieve a more favorable outcome. In this paper, we study such incentive-aware learning problem in a general setting and show that it is possible to approximately optimize the objective function under two assumptions: (i) each individual agent is a "small" (part of the market); and (ii) there is a cost associated with manipulation. For our illustrative application, this nicely translates to a mechanism for setting approximately optimal reserve prices in auctions where no individual agent has significant market share. For this application, we also show that the second assumption (that manipulations are costly) is not necessary since we can "perturb" any auction to make it costly for the agents to manipulate.
Enhancing interpretability and effectiveness in recommendation with numerical features via learning to contrast the counterfactual samples
Authors: Xiaoxiao Xu, Hao Wu, Wenhui Yu, Lantao Hu, Peng Jiang, Kun Gai
Venue: Conference WWW 2024
Year: 2024

We propose a general model-agnostic Contrastive learning framework with Counterfactual Samples Synthesizing (CCSS) for modeling the monotonicity between the neural network output and numerical features which is critical for interpretability and effectiveness of recommender systems. CCSS models the monotonicity via a two-stage process: synthesizing counterfactual samples and contrasting the counterfactual samples. The two techniques are naturally integrated into a model-agnostic framework, forming an end-to-end training process. Abundant empirical tests are conducted on a publicly available dataset and a real industrial dataset, and the results well demonstrate the effectiveness of our proposed CCSS. Besides, CCSS has been deployed in our real large-scale industrial recommender, successfully serving over hundreds of millions users.
Weisfeiler-Lehman meets Gromov-Wasserstein
Authors: Samantha Chen 0001, Sunhyuk Lim, Facundo Mémoli, Zhengchao Wan, Yusu Wang 0001
Venue: Conference ICML 2022
Year: 2022

The Weisfeiler-Lehman (WL) test is a classical procedure for graph isomorphism testing. The WL test has also been widely used both for designing graph kernels and for analyzing graph neural networks. In this paper, we propose the Weisfeiler-Lehman (WL) distance, a notion of distance between labeled measure Markov chains (LMMCs), of which labeled graphs are special cases. The WL distance is polynomial time computable and is also compatible with the WL test in the sense that the former is positive if and only if the WL test can distinguish the two involved graphs. The WL distance captures and compares subtle structures of the underlying LMMCs and, as a consequence of this, it is more discriminating than the distance between graphs used for defining the state-of-the-art Wasserstein Weisfeiler-Lehman graph kernel. Inspired by the structure of the WL distance we identify a neural network architecture on LMMCs which turns out to be universal w.r.t. continuous functions defined on the space of all LMMCs (which includes all graphs) endowed with the WL distance. Finally, the WL distance turns out to be stable w.r.t. a natural variant of the Gromov-Wasserstein (GW) distance for comparing metric Markov chains that we identify. Hence, the WL distance can also be construed as a polynomial time lower bound for the GW distance which is in general NP-hard to compute.
Trajformer: Efficient trajectory classification with transformers
Authors: Yuxuan Liang, Kun Ouyang, Yiwei Wang, Xu Liu, Hongyang Chen, Junbo Zhang, Yu Zheng, Roger Zimmermann
Venue: Conference CIKM 2022
Year: 2022

Transformers have been an efficient alternative to recurrent neural networks in many sequential learning tasks. When adapting transformers to modeling trajectories, we encounter two major issues. First, being originally designed for language modeling, transformers assume regular intervals between input tokens, which contradicts the irregularity of trajectories. Second, transformers often suffer high computational costs, especially for long trajectories. In this paper, we address these challenges by presenting a novel transformer architecture entitled TrajFormer. Our model first generates continuous point embeddings by jointly considering the input features and the information of spatio-temporal intervals, and then adopts a squeeze function to speed up the representation learning. Moreover, we introduce an auxiliary loss to ease the training of transformers using the supervision signals provided by all output tokens. Extensive experiments verify that our TrajFormer achieves a preferable speed-accuracy balance compared to existing approaches.
More than routing: Joint gps and route modeling for refine trajectory representation learning
Authors: Zhipeng Ma, Zheyan Tu, Xinhai Chen, Yan Zhang, Deguo Xia, Guyue Zhou, Yilun Chen, Yu Zheng, Jiangtao Gong
Venue: Conference WWW 2024
Year: 2024

Trajectory representation learning plays a pivotal role in supporting various downstream tasks, such as travel time estimation, trajectory classification and Top-k similar trajectory search. Traditional methods in order to filter the noise in GPS trajectories tend to focus on routing-based methods to simplify the trajectories. However, these approaches ignore the motion details contained in the GPS data, limiting the representation capability of trajectory representation learning. To fill this gap, we propose a novel representation learning framework that is Jointly G PS and Route Modeling based on self-supervised technology, namely JGRM. We consider GPS trajectory and route trajectory as the two modals of a single movement observation and fuse information through inter-modal information interaction. Specifically, we develop two encoders, each tailored to capture representations of GPS trajectories and route trajectories respectively. The representations from these two modalities are fed into a shared transformer for inter-modal information interaction. Eventually, we design three self-supervised tasks to train the model. We validate the effectiveness of the proposed method on two real-world datasets through extensive experiments. The experimental results show that JGRM significantly outperforms existing methods in both road segment representation and trajectory representation tasks. Our source code is available at Github https://github.com/mamazi0131/JGRM.