OpenRaiser/Intern-Atlas huggingface dataset:
CTnoCVR: A novelty auxiliary task making the lower- CTR-higher-CVR upper
Authors: Dandan Zhang, Haotian Wu, Guanqi Zeng, Yao Yang, Weijiang Qiu, Yujie Chen, Haoyuan Hu
Venue: Conference SIGIR 2022
Year: 2022

In recent years, multi-task learning models based on deep learning in recommender systems have attracted increasing attention from researchers in industry and academia. Accurately estimating post-click conversion rate (CVR) is often considered as the primary task of multi-task learning in recommender systems. However, some advertisers may try to get higher click-through rates (CTR) by over-decorating their ads, which may result in excessive exposure to samples with lower CVR. For example, some only eye-catching clickbait have higher CTR, but actually, CVR is very low. As a result, the overall performance of the recommender system will be hurt. In this paper, we introduce a novelty auxiliary task called CTnoCVR, which aims to predict the probability of events with click but no-conversion, in various state-of-the-art multi-task models of recommender systems to promote samples with high CVR but low CTR. Plentiful Experiments on a large-scale dataset gathered from traffic logs of Taobao's recommender system demonstrate that the introduction of CTnoCVR task significantly improves the prediction effect of CVR under various multi-task frameworks. In addition, we conduct the online test and evaluate the effectiveness of our proposed method to make those samples with high CVR and low CTR rank higher.
Adversarial learning on heterogeneous information networks
Authors: Binbin Hu, Yuan Fang, Chuan Shi
Venue: Conference KDD 2019
Year: 2019

Network embedding, which aims to represent network data in a low-dimensional space, has been commonly adopted for analyzing heterogeneous information networks (HIN). Although exiting HIN embedding methods have achieved performance improvement to some extent, they still face a few major weaknesses. Most importantly, they usually adopt negative sampling to randomly select nodes from the network, and they do not learn the underlying distribution for more robust embedding. Inspired by generative adversarial networks (GAN), we develop a novel framework HeGAN for HIN embedding, which trains both a discriminator and a generator in a minimax game. Compared to existing HIN embedding methods, our generator would learn the node distribution to generate better negative samples. Compared to GANs on homogeneous networks, our discriminator and generator are designed to be relation-aware in order to capture the rich semantics on HINs. Furthermore, towards more effective and efficient sampling, we propose a generalized generator, which samples "latent" nodes directly from a continuous distribution, not confined to the nodes in the original network as existing methods are. Finally, we conduct extensive experiments on four real-world datasets. Results show that we consistently and significantly outperform state-of-the-art baselines across all datasets and tasks.
Spatio-temporal hypergraph learning for next POI recommendation
Authors: Xiaodong Yan, Tengwei Song, Yifeng Jiao, Jianshan He, Jiaotuan Wang, Ruopeng Li, Wei Chu
Venue: Conference SIGIR 2023
Year: 2023

Next Point-of-Interest (POI) recommendation task focuses on predicting the immediate next position a user would visit, thus providing appealing location advice. In light of this, graph neural networks (GNNs) based models have recently been emerging as breakthroughs for this task due to their ability to learn global user preferences and alleviate cold-start challenges. Nevertheless, most existing methods merely focus on the relations between POIs, neglecting the higher-order information including user trajectories and the collaborative relations among trajectories. In this paper, we propose the Spatio-Temporal HyperGraph Convolutional Network (STHGCN). This model leverages a hypergraph to capture the trajectory-grain information and learn from user's historical trajectories (intra-user) as well as collaborative trajectories from other users (inter-user). Furthermore, a novel hypergraph transformer is introduced to effectively combine the hypergraph structure encoding with spatio-temporal information. Extensive experiments on real-world datasets demonstrate that our model outperforms the existing state-of-the-art methods and further analysis confirms the effectiveness in alleviating cold-start issues and achieving improved performance for both short and long trajectories.
DualFair: Fair representation learning at both group and individual levels via contrastive self-supervision
Authors: Sungwon Han, SeungEon Lee, Fangzhao Wu, Sundong Kim, Chuhan Wu, Xiting Wang, Xing Xie, Meeyoung Cha
Venue: Conference WWW 2023
Year: 2023

Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications. This work presents a self-supervised model, called DualFair, that can debias sensitive attributes like gender and race from learned representations. Unlike existing models that target a single type of fairness, our model jointly optimizes for two fairness criteria - group fairness and counterfactual fairness - and hence makes fairer predictions at both the group and individual levels. Our model uses contrastive loss to generate embeddings that are indistinguishable for each protected group, while forcing the embeddings of counterfactual pairs to be similar. It then uses a self-knowledge distillation method to maintain the quality of representation for the downstream tasks. Extensive analysis over multiple datasets confirms the model's validity and further shows the synergy of jointly addressing two fairness criteria, suggesting the model's potential value in fair intelligent Web applications.
Topology-aware retrieval augmentation for text generation
Authors: Yu Wang, Nedim Lipka, Ruiyi Zhang, Alexa F. Siu, Yuying Zhao, Bo Ni, Xin Wang, Ryan A. Rossi, Tyler Derr
Venue: Conference CIKM 2024
Year: 2024

Retrieval-augmented Generation has been used to augment Language Models by retrieving texts from external databases. Since real-world texts are often connected in the graph (e.g., papers in citation networks), we use these relations to guide the retrieval process of RAG. Concretely, we investigate proximity and role-based relations, where the former considers topologically close nodes and the latter considers structurally similar nodes. We empirically verify their correlation to text relations, which motivates us to propose the framework of Topology-aware Retrieval-augmented Generation for text generation, which consists of a retrieval module to retrieve texts by their topological relations and an aggregation module to compose retrieved texts into prompts triggering LLMs for text generation. Extensive experiments verify the effectiveness of this framework, signifying the potential of equipping RAG with topological awareness.
Learning joint relational co-evolution in spatial-temporal knowledge graph for SMEs supply chain prediction
Authors: Youru Li, Zhenfeng Zhu, Xiaobo Guo, Linxun Chen, Zhouyin Wang, Yinmeng Wang, Bing Han 0023, Yao Zhao 0001
Venue: Conference KDD 2023
Year: 2023

To effectively explore the supply chain relationships among Small and Medium-sized Enterprises (SMEs), some remarkable progress in such a relation modeling problem, especially knowledge graph-based methods have been witnessed during these years. As a typical link prediction task, supply chain prediction can usually predict the unknown future relationship facts between SMEs by utilizing the historical semantic connections between entities in knowledge graphs (KGs). However, it is still a great challenge for existing models as seldom of them can consider both temporal dependency and cooperative correlation of the connectivity pattern along the timeline synergistically. Accordingly, we propose a novel framework to learn joint relational co-evolution in Spatial-Temporal Knowledge Graphs (STKG). Specifically, on the base of the constructed large-scale financial STKG, a multi-view relational sequences mining method is proposed to reveal the semantic information from ontological concepts. Furthermore, a relational co-evolution learning module is also developed to capture the regularity of evolving connectivity patterns from the spatial-temporal view. Meanwhile, a multiple random subspace representation learning layer is also designed to improve both compatibility and complementarity during knowledge aggregation. Experimental results on large-scale SMEs supply chain prediction tasks from four real-world industries in China have illustrated the effectiveness of the proposed model.
Adaptive graph integration for cross-domain recommendation via heterogeneous graph coordinators
Authors: Hengyu Zhang, Chunxu Shen, Xiangguo Sun, Jie Tan, Yu Rong, Chengzhi Piao, Hong Cheng, Lingling Yi
Venue: Conference SIGIR 2025
Year: 2025

In the digital era, users typically interact with diverse items across multiple domains (e.g., e-commerce, streaming platforms, and social networks), generating intricate heterogeneous interaction graphs. Leveraging multi-domain data can improve recommendation systems by enriching user insights and mitigating data sparsity in individual domains. However, integrating such multi-domain knowledge for cross-domain recommendation remains challenging due to inherent disparities in user behavior and item characteristics and the risk of negative transfer, where irrelevant or conflicting information from the source domains adversely impacts the target domain's performance. To tackle these challenges, we propose HAGO, a novel framework with \textbf{H}eterogeneous \textbf{A}daptive \textbf{G}raph co\textbf{O}rdinators, which dynamically integrates multi-domain graphs into a cohesive structure. HAGO adaptively adjusts the connections between coordinators and multi-domain graph nodes to enhance beneficial inter-domain interactions while alleviating negative transfer. Furthermore, we introduce a universal multi-domain graph pre-training strategy alongside HAGO to collaboratively learn high-quality node representations across domains. Being compatible with various graph-based models and pre-training techniques, HAGO demonstrates broad applicability and effectiveness. Extensive experiments show that our framework outperforms state-of-the-art methods in cross-domain recommendation scenarios, underscoring its potential for real-world applications. The source code is available at https://github.com/zhy99426/HAGO.
Multiple Light Source Optical Flow
Authors: Robert J. Woodham
Venue: Conference ICCV 1990
Year: 1990

A novel method is described to compute a dense, local representation of optical flow. The idea is to use the intensity values recorded from multiple images of moving objects acquired simultaneously under different conditions of illumination. Each image is assumed to satisfy the standard optical flow constraint equation. Multiple images give rise to multiple constraint equations. When the optical flow and the 2-D motion field coincide, these multiple equations are in the same unknowns. A description is given of the basic theory, and the theory is illustrated on a real image motion sequence. All computations are local, independent and relatively simple. No iteration steps are required. It is suggested that the requirement to obtain simultaneous images under different conditions of illumination be satisfied by using spectrally distinct illumination and sensing.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Local Symmetries of Shapes in Arbitrary Dimension
Authors: Sibel Tari, Jayant Shah
Venue: Conference ICCV 1998
Year: 1998

Motivated by a need to define an object-centered reference system determined by the most salient characteristics of the shape, many methods have been proposed, all of which directly or indirectly involve an axis about which the shape is locally symmetric. Recently, a function /spl upsi/, called "the edge strength function", has been successfully used to determine efficiently the axes of local symmetries of 2-d shapes. The level curves of /spl upsi/ are interpreted as successively smoother versions of the initial shape boundary. The local minima of the absolute gradient /spl par//spl nabla//spl upsi//spl par/ along the level curves of /spl upsi/ are shown to be a robust criterion for determining the shape skeleton. More generally, at an extremal point of /spl par//spl nabla//spl upsi//spl par/ along a level curve, the level curve is locally symmetric with respect to the gradient vector /spl nabla//spl upsi/. That is, at such a point, the level curve is approximately a conic section whose one of the principal axes coincides with the gradient vector. Thus, the locus of the extremal points of /spl par//spl nabla//spl upsi//spl par/ along the level curves determines the axes of local symmetries of the shape. In this paper, we extend this method to shapes of arbitrary dimension.
Fast Partial Search Solution to the 3D SFM Problem
Authors: S. Srinivasan
Venue: Conference ICCV 1999
Year: 1999

In this paper, we present a robust and computationally efficient technique for estimating the focus of expansion (FOE) of an optical flow field, using fast partial search. For each candidate location on a discrete sampling of the image area, we generate a linear system of equations for determining the remaining unknowns, viz. rotation and inverse depth. We compute the least squares error of the system without actually solving the equations, to generate an error surface that describes the goodness of fit across the hypotheses. Using Fourier techniques, we prove that given an N/spl times/N flow field, the FOE can be estimated in O(N/sup 2/logN) operations. Since the resulting system is linear, bounded performances in the data lead to bounded errors. In order to demonstrate its performance on real-world problems, we apply this technique for detecting obstacles in monocular navigation imagery.
Structure and Motion from Silhouettes
Authors: Kwan-Yee Kenneth Wong, Roberto Cipolla
Venue: Conference ICCV 2001
Year: 2001

This paper addresses the problem of recovering structure and motion from silhouettes. Silhouettes are projections of contour generators which are viewpoint dependent, and hence do not readily provide point correspondences for exploitation in motion estimation. Previous works have exploited correspondences induced by epipolar tangencies, and a successful solution has been developed in the special case of circular motion (turnable sequences). However, the main drawbacks are (1) new views cannot be added easily at a later time, and (2) part of the structure will always remain invisible under circular motion. In this paper we overcome the above problems by incorporating arbitrary general views and estimating the camera poses using silhouettes alone. We present a complete and practical system which produces high quality 3D models from 2D uncalibrated silhouettes. The 3D models thus obtained can be refined incrementally by adding new arbitrary views and estimating their poses. Experimental results on various objects are presented, demonstrating the quality of the reconstructions.
Shape from the Light Field Boundary
Authors: Kiriakos N. Kutulakos
Venue: Conference CVPR 1997
Year: 1997

Ray-based representations of shape have received little attention in computer vision. In this paper we show that the problem of recovering shape from silhouettes becomes considerably simplified if it is formulated as a reconstruction problem in the space of oriented rays that intersect the object. The method can be used with both calibrated and uncalibrated cameras, does not rely on point correspondences to compute shape, and does not impose restrictions on object topology or smoothness.
Perceptual organization of occluding contours generated by opaque surfaces
Authors: Eric Saund
Venue: Conference CVPR 1999
Year: 1999

This paper offers computational theory and an algorithmic framework for perceptual organization of image contours arising from static occluding surfaces of constant lightness. We articulate constraints and biases underlying the inference of such physical events as visible surface overlap and invisible (modal and amodal) surface boundaries, from ambiguous visual evidence including visible contrast edges and L-type and T-type junctions. For any given scene, an energy or cost function is constructed over interpretation labels for nodes of a sparse graph, or belief net. Annealing-style optimization permits local cues to propagate smoothly to give rise to a global solution. We demonstrate that this approach leads to correct interpretations (in the sense of agreeing with human percepts) of popular simple "Colorforms" figures known to induce illusory contours, as well as more difficult figures where interpretations acknowledging accidental alignment are preferred.
Inductive modeling for realtime cold start recommendations
Authors: Chandler Zuo, Jonathan Castaldo, Hanqing Zhu, Haoyu Zhang, Ji Liu, Yangpeng Ou, Xiao Kong
Venue: Conference KDD 2024
Year: 2024

In recommendation systems, the timely delivery of new content to their relevant audiences is critical for generating a growing and high quality collection of content for all users. The nature of this problem requires retrieval models to be able to make inferences in real time and with high relevance. There are two specific challenges for cold start contents. First, the information loss problem in a standard Two Tower model, due to the limited feature interactions between the user and item towers, is exacerbated for cold start items due to training data sparsity. Second, the huge volume of user-generated content in industry applications today poses a big bottleneck in the end-to-end latency of recommending new content. To overcome the two challenges, we propose a novel architecture, the Item History Model (IHM). IHM directly injects user-interaction information into the item tower to overcome information loss. In addition, IHM incorporates an inductive structure using attention-based pooling to eliminate the need for recurring training, a key bottleneck for the real-timeness. On both public and industry datasets, we demonstrate that IHM can not only outperform baselines in recommending cold start contents, but also achieves SoTA real-timeness in industry applications.
Learning universal multi-level market irrationality factors to improve stock return forecasting
Authors: Chen Yang, Jingyuan Wang, Xiaohan Jiang, Junjie Wu
Venue: Conference KDD 2025
Year: 2025

Recent years have witnessed the perfect encounter of deep learning and quantitative trading has achieved great success in stock investment. Numerous deep learning-based models have been developed for forecasting stock returns, leveraging the powerful representation capabilities of neural networks to identify patterns and factors influencing stock prices. These models can effectively capture general patterns in the market, such as stock price trends, volume-price relationships, and time variations. However, the impact of special irrationality factors -- such as market sentiment, speculative behavior, market manipulation, and psychological biases -- has not been fully considered in existing deep stock forecasting models due to their relative abstraction as well as lack of explicit labels and data description. To fill this gap, we propose UMI, a Universal multi-level Market Irrationality factor model to enhance stock return forecasting. The UMI model learns factors that can reflect irrational behaviors in market from both individual stock and overall market levels. For the stock-level, UMI construct an estimated rational price for each stock, which is cointegrated with the stock's actual price. The discrepancy between the actual and the rational prices serves as a factor to indicate stock-level irrational events. Additionally, we define market-level irrational behaviors as anomalous synchronous fluctuations of stocks within a market. Using two self-supervised representation learning tasks, i.e., sub-market comparative learning and market synchronism prediction, the UMI model incorporates market-level irrationalities into a market representation vector, which is then used as the market-level irrationality factor. We also developed a forecasting model that captures both temporal and relational dependencies among stocks, accommodating the UMI factors. Extensive experiments on U.S. and Chinese stock markets with competitive baselines demonstrate our model's effectiveness and the universality of our factors in improving various forecasting models. We provide our code at https://github.com/lIcIIl/UMI.
Scene modeling using Co-Clustering
Authors: Jingen Liu, Mubarak Shah
Venue: Conference ICCV 2007
Year: 2007

In this paper, we propose a novel approach for scene modeling. The proposed method is able to automatically discover the intermediate semantic concepts. We utilize Maximization of Mutual Information (MMI) co-clustering approach to discover clusters of semantic concepts, which we call intermediate concepts. Each intermediate concept corresponds to a cluster of visterms in the bag of Vis- terms (BOV) paradigm for scene classification. MMI co- clustering results in fewer but meaningful clusters. Unlike k-means which is used to cluster image patches based on their appearances in BOV, MMI co-clustering can group the visterms which are highly correlated to some concept. Unlike probabilistic latent semantic analysis (pLSA), which can be considered as one-sided soft clustering, MMI co- clustering simultaneously clusters visterms and images, so it is able to boost both clustering. In addition, the MMI co- clustering is an unsupervised method. We have extensively tested our proposed approach on two challenging datasets: the fifteen scene categories and the LSCOM dataset, and promising results are obtained.
An objective evaluation criterion for clustering
Authors: Arindam Banerjee, John Langford
Venue: Conference KDD 2004
Year: 2004

We propose and test an objective criterion for evaluation of clustering performance: How well does a clustering algorithm run on unlabeled data aid a classification algorithm? The accuracy is quantified using the PAC-MDL bound [3] in a semisupervised setting. Clustering algorithms which naturally separate the data according to (hidden) labels with a small number of clusters perform well. A simple extension of the argument leads to an objective model selection method. Experimental results on text analysis datasets demonstrate that this approach empirically results in very competitive bounds on test set performance on natural datasets.
Design and analysis of benchmarking experiments for distributed internet services
Authors: Eytan Bakshy, Eitan Frachtenberg
Venue: Conference WWW 2015
Year: 2015

The successful development and deployment of large-scale Internet services depends critically on performance. Even small regressions in processing time can translate directly into significant energy and user experience costs. Despite the widespread use of distributed server infrastructure (e.g., in cloud computing and Web services), there is little research on how to benchmark such systems to obtain valid and precise inferences with minimal data collection costs. Correctly A/B testing distributed Internet services can be surprisingly difficult because interdependencies between user requests (e.g., for search results, social media streams, photos) and host servers violate assumptions required by standard statistical tests. We develop statistical models of distributed Internet service performance based on data from Perflab, a production system used at Facebook which vets thousands of changes to the company's codebase each day. We show how these models can be used to understand the tradeoffs between different benchmarking routines, and what factors must be taken into account when performing statistical tests. Using simulations and empirical data from Perflab, we validate our theoretical results, and provide easy-to-implement guidelines for designing and analyzing such benchmarks.