OpenRaiser/Intern-Atlas huggingface dataset:
Reserving-Masking-Reconstruction Model for Self-Supervised Heterogeneous Graph Representation
Authors: Haoran Duan 0002, Cheng Xie 0001, Linyu Li 0001
Venue: KDD
Year: 2024

Self-supervised Heterogeneous Graph Representation (SSHGRL) learning is widely used in data mining. The latest SSHGRL methods normally use metapaths to describe the heterogeneous information (multiple relations and node types) to learn the heterogeneous graph representation and achieve impressive results. However, establishing metapaths requires lofty computational costs that are too high for the medium and large graphs. To this end, this paper proposes a Reserving-Masking-Reconstruction (RMR) model that can fully consider heterogeneous information without relying on the metapaths. In detail, we propose a reserving method to reserve to-be-masked nodes' (target nodes) information before graph masking. Second, we split the reserved graph into relation subgraphs according to the type of relations that require much less computational overheads than metapath. Then, the target nodes in each relation subgraph are randomly masked with minimal topology information loss. After, a novel reconstruction method is proposed to reconstruct the masked nodes on different relation subgraphs to establish the self-supervised signal. The proposed method requires low computational complexity and can establish a self-supervised signal without deeply changing the graph topology. Experimental results show the proposed method achieves state-of-the-art records on medium and large-scale heterogeneous graphs and competitive records on small-scale heterogeneous graphs. The code is available at https://github.com/DuanhaoranCC/RMR.
CAT: Interpretable Concept-based Taylor Additive Models
Authors: Viet Duong, Qiong Wu 0008, Zhengyi Zhou, Hongjue Zhao, Chenxiang Luo, Eric Zavesky, Huaxiu Yao, Huajie Shao
Venue: KDD
Year: 2024

As an emerging interpretable technique, Generalized Additive Models (GAMs) adopt neural networks to individually learn non-linear functions for each feature, which are then combined through a linear model for final predictions. Although GAMs can explain deep neural networks (DNNs) at the feature level, they require large numbers of model parameters and are prone to overfitting, making them hard to train and scale. Additionally, in real-world datasets with many features, the interpretability of feature-based explanations diminishes for humans. To tackle these issues, recent research has shifted towards concept-based interpretable methods. These approaches try to integrate concept learning as an intermediate step before making predictions, explaining the predictions in terms of human-understandable concepts. However, these methods require domain experts to extensively label concepts with relevant names and their ground-truth values. In response, we propose CAT, a novel interpretable Concept-bAsed Taylor additive model to simplify this process. CAT does not require domain experts to annotate concepts and their ground-truth values. Instead, it only requires users to simply categorize input features into broad groups, which can be easily accomplished through a quick metadata review. Specifically, CAT first embeds each group of input features into one-dimensional high-level concept representation, and then feeds the concept representations into a new white-box Taylor Neural Network (TaylorNet). The TaylorNet aims to learn the non-linear relationship between the inputs and outputs using polynomials. Evaluation results across multiple benchmarks demonstrate that CAT can outperform or compete with the baselines while reducing the need of extensive model parameters. Importantly, it can effectively explain model predictions through high-level concepts. Source code is available at github.com/vduong143/CAT-KDD-2024.
Enhancing E-commerce Spelling Correction with Fine-Tuned Transformer Models
Authors: Arnab Dutta 0005, Gleb Polushin, Xiaoshuang Zhang, Daniel Stein
Venue: KDD
Year: 2024

In the realm of e-commerce, the process of search stands as the primary point of interaction for users, wielding a profound influence on the platform's revenue generation. Notably, spelling correction assumes a pivotal role in shaping the user's search experience by rectifying erroneous query inputs, thus facilitating more accurate retrieval outcomes. Within the scope of this research paper, our aim is to enhance the existing state-of-the-art discriminative model performance with generative modelling strategies while concurrently addressing the engineering concerns associated with real-time online latency, inherent to models of this category. We endeavor to refine LSTM-based classification models for spelling correction through a generative fine-tuning approach hinged upon pre-trained language models. Our comprehensive offline assessments have yielded compelling results, showcasing that transformer-based architectures, such as BART (developed by Facebook) and T5 (a product of Google), have achieved a 4% enhancement in F1 score compared to baseline models for the English language sites. Furthermore, to mitigate the challenges posed by latency, we have incorporated model pruning techniques like no-teacher distillation. We have undertaken the deployment of our model (English only) as an A/B test candidate for real-time e-commerce traffic, encompassing customers from the US and the UK. The model attest to a 100% successful request service rate within real-time scenarios, with median, 90th percentile, and 99th percentile (p90/p99) latencies comfortably falling below production service level agreements. Notably, these achievements are further reinforced by positive customer engagement, transactional and search page metrics, including a significant reduction in instances of search results page with low or almost zero recall. Moreover, we have also extended our efforts into fine-tuning a multilingual model, which, notably, exhibits substantial accuracy enhancements, amounting to a minimum of 16%, across four distinct European languages and English.
Fragile Earth: Generative and Foundational Models for Sustainable Development
Authors: Emre Eftelioglu, Bistra Dilkina, Naoki Abe, Ramakrishnan Kannan, Yuzhou Chen, Yulia R. Gel, Kathleen Buckingham, Auroop R. Ganguly, James Hodson 0003, Jiafu Mao
Venue: KDD
Year: 2024

The Fragile Earth Workshop is a recurring event in ACM's KDD Conference on research in knowledge discovery and data mining that gathers the research community to find and explore how data science can measure and progress climate and social issues, following the United Nations Sustainable Development Goals (SDGs) framework.
A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models
Authors: Wenqi Fan, Yujuan Ding, Liangbo Ning 0001, Shijie Wang 0002, Hengyun Li, Dawei Yin 0001, Tat-Seng Chua, Qing Li 0001
Venue: KDD
Year: 2024

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge, providing huge convenience for numerous tasks. Particularly in the era of AI-Generated Content (AIGC), the powerful capacity of retrieval in providing additional knowledge enables RAG to assist existing generative AI in producing high-quality outputs. Recently, Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation, while still facing inherent limitations such as hallucinations and out-of-date internal knowledge. Given the powerful abilities of RAG in providing the latest and helpful auxiliary information, Retrieval-Augmented Large Language Models (RA-LLMs) have emerged to harness external and authoritative knowledge bases, rather than solely relying on the model's internal knowledge, to augment the quality of the generated content of LLMs. In this survey, we comprehensively review existing research studies in RA-LLMs, covering three primary technical perspectives: Furthermore, to deliver deeper insights, we discuss current limitations and several promising directions for future research. Updated information about this survey can be found at: https://advanced-recommender-systems.github.io/RAG-Meets-LLMs/
Label Shift Correction via Bidirectional Marginal Distribution Matching
Authors: Ruidong Fan, Xiao Ouyang, Hong Tao, Chenping Hou
Venue: KDD
Year: 2024

Due to the timeliness and uncertainty of data acquisition, label shift, which assumes that the source (training) and target (test) label distributions differ, occurs with the changing environment and reduces the generalization ability of traditional models. To correct the label shift, existing methods estimate the true label distribution by prediction of target data from a source classifier, which results in high variance, especially with large label shift. In this paper, we tackle this problem by proposing a novel approach termed as Label Shift Correction via Bidirectional Marginal Distribution Matching (BMDM). Our approach matchs the label and feature marginal distributions simultaneously to ensure the stability of estimated class proportions. We prove theoretically that there is a unique optimal solution, i.e., true target label distribution, for our approach under mild conditions, and an efficient optimization strategy is also proposed. On this basis, in multi-shot scenario where label distribution changes continuously, we extend BMDM by designing a new distribution matching mechanism and constructing a regularization term that constrains the direction of label distribution change. Extensive experimental results validate the effectiveness of our approach over existing state-of-the-arts methods.
ROTAN: A Rotation-based Temporal Attention Network for Time-Specific Next POI Recommendation
Authors: Shanshan Feng 0001, Feiyu Meng, Lisi Chen 0001, Shuo Shang, Yew Soon Ong
Venue: KDD
Year: 2024

The next Point-of-interest recommendation has attracted extensive research interest recently, which predicts users' subsequent movements. The main challenge is how to effectively capture users' personalized sequential transitions in check-in trajectory, and various methods have been developed. However, most existing studies ignore the temporal information when conducting the next POI recommendation. To fill this gap, we investigate a time-specific next POI recommendation task, which additionally incorporates the target time information. We propose a brand new Time2Rotation technique to capture the temporal information. Different from conventional methods, we represent timeslots as rotation vectors and then perform the rotation operations. Based on the Time2Rotation technique, we propose a novel rotation-based temporal attention network, namely ROTAN, for the time-specific next POI recommendation task. The ROTAN begins by building a collaborative POI transition graph, capturing the asymmetric temporal influence in sequential transitions. After that, it incorporates temporal information into the modeling of individual check-in trajectories, extracting separate representations for user preference and POI influence to reflect their distinct temporal patterns. Lastly, the target time is integrated to generate recommendations. Extensive experiments are conducted on three real-world datasets, which demonstrates the advantages of the proposed Time2Rotation technique and ROTAN recommendation model.
Influence Maximization via Graph Neural Bandits
Authors: Yuting Feng, Vincent Y. F. Tan, Bogdan Cautis
Venue: KDD
Year: 2024

We consider a ubiquitous scenario in the study of Influence Maximization (IM), in which there is limited knowledge about the topology of the diffusion network. We set the IM problem in a multi-round diffusion campaign, aiming to maximize the number of distinct users that are influenced. Leveraging the capability of bandit algorithms to effectively balance the objectives of exploration and exploitation, as well as the expressivity of neural networks, our study explores the application of neural bandit algorithms to the IM problem. We propose the framework IM-GNB (Influence Maximization with Graph Neural Bandits), where we provide an estimate of the users' probabilities of being influenced by influencers (also known as diffusion seeds). This initial estimate forms the basis for constructing both an exploitation graph and an exploration one. Subsequently, IM-GNB handles the exploration-exploitation tradeoff, by selecting seed nodes in real-time using Graph Convolutional Networks (GCN), in which the pre-estimated graphs are employed to refine the influencers' estimated rewards in each contextual setting. Through extensive experiments on two large real-world datasets, we demonstrate the effectiveness of IM-GNB compared with other baseline methods, significantly improving the spread outcome of such diffusion campaigns, when the underlying network is unknown.
Communication-efficient Multi-service Mobile Traffic Prediction by Leveraging Cross-service Correlations
Authors: Zhiying Feng, Qiong Wu 0009, Xu Chen 0004
Venue: KDD
Year: 2024

Mobile traffic prediction plays a crucial role in enabling efficient network management and service provisioning. Traditional prediction approaches treat different mobile application services (such as Uber, Facebook, Twitter, etc) as isolated entities, neglecting potential correlation among them. Moreover, such isolated prediction methods necessitate the uploading of historical traffic data from all regions to forecast city-wide traffic, resulting in consuming substantial bandwidth resources and risking prediction failure in the event of data loss in specific regions. To address these challenges, we propose a novel Cross-service Attention-based Spatial-Temporal Graph Convolutional Network (CsASTGCN) for precise and communication-efficient multi-service mobile traffic prediction. Our methodology allows each mobile service to transmit the traffic data of only a fraction of regions for city-wide traffic prediction of all mobile services, which reduces the resource consumption caused by data transmission. Specifically, the sparse traffic data are initially transmitted to the cloud server and the masked graph autoencoder is utilized to roughly reconstruct the traffic volume for regions with missing data. Subsequently, a cross-service attention-based predictor is designed to calculate the data correlation among different mobile services within the same region. Considering the constantly emerging mobile services, we incorporate a novel model-based adaptive transfer learning scheme to extract valuable knowledge from the existing models and expedite the training of a new model for a new service without training from scratch, thereby enhancing the scalability of our framework. Extensive experiments conducted on a large-scale real-world mobile traffic dataset demonstrate that our model greatly outperforms the existing schemes, enhancing both the communication-efficiency and robustness of large-scale multi-service traffic prediction.
SensitiveHUE: Multivariate Time Series Anomaly Detection by Enhancing the Sensitivity to Normal Patterns
Authors: Yuye Feng, Wei Zhang 0387, Yao Fu 0006, Weihao Jiang, Jiang Zhu, Wenqi Ren
Venue: KDD
Year: 2024

Unsupervised anomaly detection in multivariate time series (MTS) has always been a challenging problem, and the modeling based on reconstruction has garnered significant attention. The insensitivity of these methods towards normal patterns poses challenges in distinguishing between normal and abnormal points. Firstly, the general reconstruction strategies may exhibit limited sensitivity to spatio-temporal dependencies, and their performance remains largely unaffected by such dependencies. Secondly, most methods fail to model the heteroscedastic uncertainty in MTS, hindering their abilities to derive a distinguishable criterion. For instance, normal data with high noise levels may lead to detection failure due to excessively high reconstruction errors. In this work, we emphasize the necessity of sensitivity to normal patterns, which could improve the discrimination between normal and abnormal points remarkably. To this end, we propose SensitiveHUE, a probabilistic network by implementing both reconstruction and heteroscedastic uncertainty estimation. Its core includes a statistical feature removal strategy to ensure the dependency sensitive property, and a novel MTS-NLL loss for modeling the normal patterns in important regions. Experimental results demonstrate that SensitiveHUE exhibits nontrivial sensitivity to normal patterns and outperforms the existing state-of-the-art alternatives by a large margin. Code is publicly available at this URL\footnotehttp://github.com/yuesuoqingqiu/SensitiveHUE.
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine Learning
Authors: Ulrik Friis-Jensen, Frederik L. Johansen, Andy S. Anker, Erik B. Dam, Kirsten M. Ø. Jensen, Raghavendra Selvan
Venue: KDD
Year: 2024

Advances in graph machine learning (ML) have been driven by applications in chemistry as graphs have remained the most expressive representations of molecules. While early graph ML methods focused primarily on small organic molecules, recently, the scope of graph ML has expanded to include inorganic materials. Modelling the periodicity and symmetry of inorganic crystalline materials poses unique challenges, which existing graph ML methods are unable to address. Moving to inorganic nanomaterials increases complexity as the scale of number of nodes within each graph can be broad ($10$ to $10^5$). The bulk of existing graph ML focuses on characterising molecules and materials by predicting target properties with graphs as input. However, the most exciting applications of graph ML will be in their generative capabilities, which is currently not at par with other domains such as images or text. We invite the graph ML community to address these open challenges by presenting two new chemically-informed large-scale inorganic (CHILI) nanomaterials datasets: A medium-scale dataset (with overall >6M nodes, >49M edges) of mono-metallic oxide nanomaterials generated from 12 selected crystal types (CHILI-3K) and a large-scale dataset (with overall >183M nodes, >1.2B edges) of nanomaterials generated from experimentally determined crystal structures (CHILI-100K). We define 11 property prediction tasks and 6 structure prediction tasks, which are of special interest for nanomaterial research. We benchmark the performance of a wide array of baseline methods and use these benchmarking results to highlight areas which need future work. To the best of our knowledge, CHILI-3K and CHILI-100K are the first open-source nanomaterial datasets of this scale -- both on the individual graph level and of the dataset as a whole -- and the only nanomaterials datasets with high structural and elemental diversity.
Residual Multi-Task Learner for Applied Ranking
Authors: Cong Fu 0001, Kun Wang 0024, Jiahua Wu 0003, Yizhou Chen, Guangda Huzhang, Yabo Ni, Anxiang Zeng, Zhiming Zhou 0002
Venue: KDD
Year: 2024

Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services.Consequently, multitask learning has become an integral part of their ranking systems.However, existing multi-task learning methods encounter two main challenges: some lack explicit modeling of task relationships, resulting in inferior performance, while others have limited applicability due to being computationally intensive, having scalability issues, or relying on strong assumptions.To address these limitations and better fit our real-world scenario, pre-rank in Shopee Search, we introduce in this paper ResFlow, a lightweight multi-task learning framework that enables efficient cross-task information sharing via residual connections between corresponding layers of task networks.Extensive experiments on datasets from various scenarios and modalities demonstrate its superior performance and adaptability over state-of-the-art methods.The online A/B tests in Shopee Search showcase its practical value in large-scale industrial applications, evidenced by a 1.29% increase in OPU (order-per-user) without additional system latency.ResFlow is now fully deployed in the pre-rank module of Shopee Search.To facilitate efficient online deployment, we propose a novel offline metric Weighted Recall@K, which aligns well with our online metric OPU, addressing the longstanding online-offline metric misalignment issue.Besides, we propose to fuse scores from the multiple tasks additively when ranking items, which outperforms traditional multiplicative fusion.
Policy-Based Bayesian Active Causal Discovery with Deep Reinforcement Learning
Authors: Heyang Gao, Zexu Sun, Hao Yang 0045, Xu Chen 0017
Venue: KDD
Year: 2024

Causal discovery with observational and interventional data plays an important role in numerous fields. Due to the costly and potentially risky nature of intervention experiments, selecting informative interventions is critical in real-world situations. Several recent works introduce Bayesian active learning to select interventions that maximize the expected information gain about the underlying causal relationship at each optimization step. However, there are still some limitations within these methods: (1) Local optimality. With multiple intervention experiments, selecting optimal intervention myopically at each step may drop into the local optimal point. (2) Expensive time cost. Optimizing the most informative intervention at each step is time-consuming and not suitable for adaptive experiments with strict inference speed requirements. In this study, we propose a novel method called Reinforcement Learning-based Causal Bayesian Experimental Design (RL-CBED) to reduce the risk of local optimality and accelerate intervention selection inference. Specifically, we formulate the active causal discovery problem as a partially observable Markov decision process (POMDP). We design an information gain-based sparse reward function and then improve it to a dense reward function, providing fine-grained feedback to help the RL policy learn more quickly in complex environments. Moreover, we theoretically prove that the Q-function estimator can be learned using only trajectories sampled from the prior, which can significantly reduce the time cost of training process, enabling the real-world application of our method. Extensive experiments on both synthetic and real world-inspired semi-synthetic datasets demonstrate the effectiveness of our proposed method.
Online Preference Weight Estimation Algorithm with Vanishing Regret for Car-Hailing in Road Network
Authors: Yucen Gao, Zhehao Zhu, Mingqian Ma, Fei Gao, Hui Gao, Yangguang Shi, Xiaofeng Gao 0001
Venue: KDD
Year: 2024

Car-hailing services play an important role in the modern transportation system, and the utilities of the service providers highly depend on the efficiency of route planning algorithms. A widely adopted route planning framework is to assign weights to roads and compute the routes with the shortest path algorithms. Existing techniques of weight-assigning often focus on the traveling time and length of the roads, but cannot incorporate with the preferences of the passengers (users).
KiL 2024: 4th International Workshop on Knowledge-infused Learning (Towards Consistent, Reliable, Explainable, and Safe LLMs)
Authors: Manas Gaur, Efthymia Tsamoura, Edward Raff, Nikhita Vedula, Srinivasan Parthasarathy 0001
Venue: KDD
Year: 2024

KDD ’24: 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, August 25-29, 2024, Barcelona, Spain
Machine Learning for Clinical Management: From the Lab to the Hospital
Authors: Ricard Gavaldà
Venue: KDD
Year: 2024

Population aging, increasing social demands, and rising costs of treatments are stressing healthcare systems to the point of risking the sustainability of universal and accessible healthcare.