OpenRaiser/Intern-Atlas huggingface dataset:
UA-FedRec: Untargeted attack on federated news recommendation
Authors: Jingwei Yi, Fangzhao Wu, Bin Zhu 0008, Jing Yao 0003, Zhulin Tao, Guangzhong Sun, Xing Xie 0001
Venue: Conference KDD 2023
Year: 2023

News recommendation is essential for personalized news distribution. Federated news recommendation, which enables collaborative model learning from multiple clients without sharing their raw data, is a promising approach for preserving users' privacy. However, the security of federated news recommendation is still unclear. In this paper, we study this problem by proposing an untargeted attack on federated news recommendation called UA-FedRec. By exploiting the prior knowledge of news recommendation and federated learning, UA-FedRec can effectively degrade the model performance with a small percentage of malicious clients. First, the effectiveness of news recommendation highly depends on user modeling and news modeling. We design a news similarity perturbation method to make representations of similar news farther and those of dissimilar news closer to interrupt news modeling, and propose a user model perturbation method to make malicious user updates in opposite directions of benign updates to interrupt user modeling. Second, updates from different clients are typically aggregated with a weighted average based on their sample sizes. We propose a quantity perturbation method to enlarge sample sizes of malicious clients in a reasonable range to amplify the impact of malicious updates. Extensive experiments on two real-world datasets show that UA-FedRec can effectively degrade the accuracy of existing federated news recommendation methods, even when defense is applied. Our study reveals a critical security issue in existing federated news recommendation systems and calls for research efforts to address the issue. Our code is available at https://github.com/yjw1029/UA-FedRec.
Multi-Sequence Attentive User Representation Learning for Side-information Integrated Sequential Recommendation
Authors: Xiaolin Lin, Jinwei Luo, Junwei Pan, Weike Pan, Zhong Ming, Xun Liu, Shudong Huang, Jie Jiang
Venue: Conference WSDM 2024
Year: 2024

Side-information integrated sequential recommendation incorporates supplementary information to alleviate the issue of data sparsity. The state-of-the-art works mainly leverage some side information to improve the attention calculation to learn user representation more accurately. However, there are still some limitations to be addressed in this topic. Most of them merely learn the user representation at the item level and overlook the association of the item sequence and the side-information sequences when calculating the attentions, which results in the incomprehensive learning of user representation. Some of them learn the user representations at both the item and side-information levels, but they still face the problem of insufficient optimization of multiple user representations. To address these limitations, we propose a novel model, i.e., Multi-Sequence Sequential Recommender (MSSR), which learns the user's multiple representations from diverse sequences. Specifically, we design a multi-sequence integrated attention layer to learn more attentive pairs than the existing works and adaptively fuse these pairs to learn user representation. Moreover, our user representation alignment module constructs the self-supervised signals to optimize the representations. Subsequently, they are further refined by our side information predictor during training. For item prediction, our MSSR extra considers the side information of the candidate item, enabling a comprehensive measurement of the user's preferences. Extensive experiments on four public datasets show that our MSSR outperforms eleven state-of-the-art baselines. Visualization and case study also demonstrate the rationality and interpretability of our MSSR.
Large language model simulator for cold-start recommendation
Authors: Feiran Huang, Yuanchen Bei, Zhenghang Yang, Junyi Jiang, Hao Chen, Qijie Shen, Senzhang Wang, Fakhri Karray, Philip S. Yu
Venue: Conference WSDM 2025
Year: 2025

Recommending cold items remains a significant challenge in billion-scale online recommendation systems. While warm items benefit from historical user behaviors, cold items rely solely on content features, limiting their recommendation performance and impacting user experience and revenue. Current models generate synthetic behavioral embeddings from content features but fail to address the core issue: the absence of historical behavior data. To tackle this, we introduce the LLM Simulator framework, which leverages large language models to simulate user interactions for cold items, fundamentally addressing the cold-start problem. However, simply using LLM to traverse all users can introduce significant complexity in billion-scale systems. To manage the computational complexity, we propose a coupled funnel ColdLLM framework for online recommendation. ColdLLM efficiently reduces the number of candidate users from billions to hundreds using a trained coupled filter, allowing the LLM to operate efficiently and effectively on the filtered set. Extensive experiments show that ColdLLM significantly surpasses baselines in cold-start recommendations, including Recall and NDCG metrics. A two-week A/B test also validates that ColdLLM can effectively increase the cold-start period GMV.
Hierarchical projection enhanced multi-behavior recommendation
Authors: Chang Meng, Hengyu Zhang 0001, Wei Guo 0006, Huifeng Guo, Haotian Liu, Yingxue Zhang 0001, Hongkun Zheng, Ruiming Tang, Xiu Li 0001, Rui Zhang 0003
Venue: Conference KDD 2023
Year: 2023

Various types of user behaviors are recorded in most real-world recommendation scenarios. To fully utilize the multi-behavior information, the exploration of multiplex interaction among them is essential. Many multi-task learning based multi-behavior methods are proposed recently to use multiple types of supervision signals and perform information transfer among them. Despite the great successes, these methods fail to design prediction tasks comprehensively, leading to insufficient utilization of multi-behavior correlative information. Besides, these methods are either based on the weighting of expert information extracted from the coupled input or modeling of information transfer between multiple behavior levels through task-specific extractors, which are usually accompanied by negative transfer phenomenon1. To address the above problems, we propose a multi-behavior recommendation framework, called Hierarchical Projection Enhanced Multi-behavior Recommendation (HPMR). The key module, Projection-based Transfer Network (PTN), uses the projection mechanism to "explicitly" model the correlations of upstream and downstream behaviors, refines the upstream behavior representations, and fully uses the refined representations to enhance the learning of downstream tasks. Offline experiments on public and industrial datasets and online A/B test further verify the effectiveness of HPMR in modeling the associations from upstream to downstream and alleviating the negative transfer. The source code and datasets are available at https://github.com/MC-CV/HPMR.
Data-centric AI: Techniques and future perspectives
Authors: Daochen Zha, Kwei-Herng Lai, Fan Yang 0023, Na Zou 0001, Huiji Gao, Xia Hu 0001
Venue: Conference KDD 2023
Year: 2023

The role of data in AI has been significantly magnified by the emerging concept of data-centric AI. In contrast to the traditional model-centric paradigm, which focuses on developing more effective models given fixed datasets, data-centric AI emphasizes the systematic engineering of data in building AI systems. However, as a new concept, many critical aspects of data-centric AI remain ambiguous, such as its definitions, associated tasks, algorithms, challenges, and benchmarks. This tutorial aims to review and discuss this emerging field, with a particular focus on the three general data-centric AI goals: training data development, inference data development, and data maintenance. The objective of this tutorial is threefold: (1) to formally categorize the field of data-centric AI using a goal-driven taxonomy and discuss the needs and challenges of each goal, (2) to comprehensively review the state-of-the-art techniques, and (3) to discuss the future perspectives and open research directions to inspire further innovations in this field.
Detection of convex and concave discontinuous points in a plane curve
Authors: Joon H. Han
Venue: Conference ICCV 1990
Year: 1990

A simple and reliable method is presented for detecting concave and convex discontinuous points in plane curves. The method can be applied to detect feature points in 2-D binary images or it can be used in detecting surface boundaries in range images. The author also shows that the impulse response of the detection method is similar to that of the second derivative of a Gaussian operator. Some experimental results are given with 2-D images and range images.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Dual space graph contrastive learning
Authors: Haoran Yang, Hongxu Chen, Shirui Pan, Lin Li, Philip S. Yu, Guandong Xu
Venue: Conference WWW 2022
Year: 2022

Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive learning is one of the unsupervised graph representation learning methods, which recently attracts attention from researchers and has achieved state-of-the-art performances on various tasks. The key to the success of graph contrastive learning is to construct proper contrasting pairs to acquire the underlying structural semantics of the graph. However, this key part is not fully explored currently, most of the ways generating contrasting pairs focus on augmenting or perturbating graph structures to obtain different views of the input graph. But such strategies could degrade the performances via adding noise into the graph, which may narrow down the field of the applications of graph contrastive learning. In this paper, we propose a novel graph contrastive learning method, namely \textbf{D}ual \textbf{S}pace \textbf{G}raph \textbf{C}ontrastive (DSGC) Learning, to conduct graph contrastive learning among views generated in different spaces including the hyperbolic space and the Euclidean space. Since both spaces have their own advantages to represent graph data in the embedding spaces, we hope to utilize graph contrastive learning to bridge the spaces and leverage advantages from both sides. The comparison experiment results show that DSGC achieves competitive or better performances among all the datasets. In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.
Decoupled variational graph autoencoder for link prediction
Authors: Yoon-Sik Cho
Venue: Conference WWW 2024
Year: 2024

Link prediction is an important learning task for graph-structured data, and has become increasingly popular due to its wide application areas. Graph Neural Network (GNN)-based approaches including Variational Graph Autoencoder (VGAE) have achieved promising performance on link prediction outperforming conventional models which use hand-crafted features. VGAE learns latent node representations and predicts links based on the similarities between nodes. While the inner product based decoder effectively utilizes the node representations for link prediction, it exhibits sub-optimal performance due to the intrinsic limitation of the inner product. We found that the the cosine similarity and norm simultaneously try to explain the link probability, which hinders the gradient flow during training. We also point out the message passing scheme is unexpectedly dominated by the nodes with large norm values. In this paper, we propose a stochastic VGAE-based method that can effectively decouple the norm and angle in the embeddings. Specifically, we relate the cosine similarity and norm to two fundamental principles in graph: homophily and node popularity respectively. Our learning scheme is based on a hard expectation maximization learning method; we infer which of the two has been exerted for link formation, and subsequently optimize based on this guess. Through extensive experiments on real-world datasets, we demonstrate our model outperforms the existing state-of-the-art methods on link prediction and achieves comparable performances on other downstream tasks such as node classification and clustering. Our code is at https://github.com/yoonsikcho/d-vgae.
Killing two birds with one stone: Cross-modal reinforced prompting for graph and language tasks
Authors: Wenyuan Jiang, Wenwei Wu, Le Zhang 0010, Zixuan Yuan, Jian Xiang, Jingbo Zhou 0003, Hui Xiong 0001
Venue: Conference KDD 2024
Year: 2024

In recent years, Graph Neural Networks (GNNs) and Large Language Models (LLMs) have exhibited remarkable capability in addressing different graph learning and natural language tasks, respectively. Motivated by this, integrating LLMs with GNNs has been increasingly studied to acquire transferable knowledge across modalities, which leads to improved empirical performance in language and graph domains. However, existing studies mainly focused on a single-domain scenario by designing complicated integration techniques to manage multimodal data effectively. Therefore, a concise and generic learning framework for multi-domain tasks, i.e., graph and language domains, is highly desired yet remains under-exploited due to two major challenges. First, the language corpus of downstream tasks differs significantly from graph data, making it hard to bridge the knowledge gap between modalities. Second, not all knowledge demonstrates immediate benefits for downstream tasks, potentially introducing disruptive noise to context-sensitive models like LLMs. To tackle these challenges, we propose a novel plug-and-play framework for incorporating a lightweight cross-domain prompting method into both language and graph learning tasks. Specifically, we first convert the textual input into a domain-scalable prompt, which not only preserves the semantic and logical contents of the textual input, but also highlights related graph information as external knowledge for different domains. Then, we develop a reinforcement learning-based method to learn the optimal edge selection strategy for useful knowledge extraction, which profoundly sharpens the multi-domain model capabilities. In addition, we introduce a joint multi-view optimization module to regularize agent-level collaborative learning across two domains. Finally, extensive empirical justifications over 23 public and synthetic datasets demonstrate that our approach can be applied to diverse multi-domain tasks more accurately, robustly, and reasonably, and improve the performances of the state-of-the-art graph and language models in different learning paradigms.
GCoT: Chain-of-thought prompt learning for graphs
Authors: Xingtong Yu, Chang Zhou, Zhongwei Kuai, Xinming Zhang, Yuan Fang
Venue: Conference KDD 2025
Year: 2025

Chain-of-thought (CoT) prompting has achieved remarkable success in natural language processing (NLP). However, its vast potential remains largely unexplored for graphs. This raises an interesting question: How can we design CoT prompting for graphs to guide graph models to learn step by step? On one hand, unlike natural languages, graphs are non-linear and characterized by complex topological structures. On the other hand, many graphs lack textual data, making it difficult to formulate language-based CoT prompting. %Therefore we cannot directly adopt the CoT prompting methods used in the language domain. In this work, we propose the first CoT prompt learning framework for text-free graphs, GCoT. Specifically, we decompose the adaptation process for each downstream task into a series of inference steps, with each step consisting of prompt-based inference, ''thought'' generation, and thought-conditioned prompt learning. While the steps mimic CoT prompting in NLP, the exact mechanism differs significantly. Specifically, at each step, an input graph, along with a prompt, is first fed into a pre-trained graph encoder for prompt-based inference. We then aggregate the hidden layers of the encoder to construct a ''thought'', which captures the working state of each node in the current step. Conditioned on this thought, we learn a prompt specific to each node based on the current state. These prompts are fed into the next inference step, repeating the cycle. To evaluate and analyze the effectiveness of GCoT, we conduct comprehensive experiments on eight public datasets, which demonstrate the advantage of our approach.
Image Registration by Aligning Entropies
Authors: Clark F. Olson
Venue: Conference CVPR 2001
Year: 2001

Maximization of mutual information is a powerful method for registering images (and other data) captured with different sensors or under varying conditions, since the technique is robust to variations in the image formation process. On the other hand, the high level of robustness allows false positives when matching over a large search space and also makes it difficult to formulate an efficient search strategy for this case. We describe techniques to overcome these problems by aligning image entropies, which are robust to illumination variation and can be applied to multi-sensor registration. This results in a lower rate of false positives and a more efficient method to search an image for the matching position. The techniques are applied to real imagery and compared to methods based on mutual information and gradients to demonstrate their effectiveness.
Object Detection with Vector Quantized Binary Features
Authors: John Krumm
Venue: Conference CVPR 1997
Year: 1997

This paper presents a new algorithm for detecting objects in images, one of the fundamental tasks of computer vision. The algorithm extends the representational efficiency of eigenimage methods to binary features, which are less sensitive to illumination changes than gray-level values normally used with eigenimages. Binary features (square subtemplates) are automatically chosen on each training image. Using features rather than whole templates makes the algorithm more robust to background clutter and partial occlusions. Instead of representing the features with real-valued eigenvector principle components, we use binary vector quantization to avoid floating point computations. The object is detected in the image using a simple geometric hash table and Hough transform. On a test of 1000 images, the algorithm works on 99.3%. We present a theoretical analysis of the algorithm in terms of the receiver operating characteristic, which consists of the probabilities of detection and false alarm. We verify this analysis with the results of our 1000-image test and we use the analysis as a principled way to select some of the algorithm's important operating parameters.
Video Stabilization as a Variational Problem and Numerical Solution with the Viterbi Method
Authors: Maurizio Pilu
Venue: Conference CVPR 2004
Year: 2004

This paper presents a new method for video stabilization based on a variational formulation that produces optimally stable sequences independently of the input motion but within the hard constraints imposed by finite sensor size to the maximum allowed compensatory transformation. A practical and fast numerical solution using the Viterbi algorithm is also proposed. The results show the efficacy of the method compared to a class of previous approaches.
BRDF/BTF Measurement Device
Authors: Kristin J. Dana
Venue: Conference ICCV 2001
Year: 2001

Capturing surface appearance is important for a large number of applications. Appearance of real world surfaces is difficult to model as it varies with the direction of illumination as well as the direction from which it is viewed. Consequently, measurements of the BRDF (bidirectional reflectance distribution function) have been important. In addition, many applications require measuring how the entire surface reflects light, i.e. spatially varying BRDF measurements are important as well. For compactness we refer to a spatially varying BRDF as a BTF (bidirectional texture function). Measurements of BRDF and/or BTF typically require significant resources in time and equipment. In this work, a device for BRDF/BTF measurement is presented that is compact, economical and convenient. The device uses the approach of curved mirrors to remove the need for hemispherical positioning of the camera and illumination source. Instead, simple planar translations of optical components are used to vary the illumination direction and to scan the surface. Furthermore, the measurement process is fast because the device enables simultaneous measurements of multiple viewing directions.
Tracking People and Recognizing Their Activities
Authors: Deva Ramanan, David A. Forsyth, Andrew Zisserman
Venue: Conference CVPR 2005
Year: 2005

We present a system for automatic people tracking and activity recognition. Our basic approach to people-tracking is to build an appearance model for the person in the video. The video illustrates our method of using a stylized-pose detector. Our system builds a model of limb appearance from those sparse stylized detections. Our algorithm then reprocesses the video, using the learned appearance models to find people in unrestricted configuration. We can use our tracker to recover 3D configurations and activity labels. We assume we have a motion capture library where the 3D poses have been labeled offline with activity descriptions.
Joint Feature-Basis Subset Selection
Authors: Shai Avidan
Venue: Conference CVPR 2004
Year: 2004

We treat feature selection and basis selection in a unified framework by introducing the masking matrix. If one considers feature selection as finding a binary mask vector that determines which features participate in the learning process, and similarly, basis selection as finding a binary mask vector that determines which basis vectors are needed for the learning process, then the masking matrix is, in particular, the outer product of the feature masking vector and the basis masking vector. This representation allows for a joint estimation of both features and basis. In addition, it allows one to select features that appear in only part of the basis functions. This joint selection of feature/basis subset is not possible when using feature selection and basis selection algorithms independently, thus, the masking matrix help extend feature and basis selection methods while blurring the lines between them. The problem of searching for an optimal masking matrix is NP-hand and we offer a sub-optimal probabilistic method to find it. In particular we demonstrate our ideas on the problem of feature and basis selection for SVM classification and show results for the problem of image classification on faces and vehicles.