OpenRaiser/Intern-Atlas huggingface dataset:
MANA-Net: Mitigating Aggregated Sentiment Homogenization with News Weighting for Enhanced Market Prediction
Authors: Mengyu Wang, Tiejun Ma
Venue: CIKM
Year: 2024

It is widely acknowledged that extracting market sentiments from news data benefits market predictions. However, existing methods of using financial sentiments remain simplistic, relying on equal-weight and static aggregation to manage sentiments from multiple news items. This leads to a critical issue termed "Aggregated Sentiment Homogenization'', which has been explored through our analysis of a large financial news dataset from industry practice. This phenomenon occurs when aggregating numerous sentiments, causing representations to converge towards the mean values of sentiment distributions and thereby smoothing out unique and important information. Consequently, the aggregated sentiment representations lose much predictive value of news data. To address this problem, we introduce the Market Attention-weighted News Aggregation Network (MANA-Net), a novel method that leverages a dynamic market-news attention mechanism to aggregate news sentiments for market prediction. MANA-Net learns the relevance of news sentiments to price changes and assigns varying weights to individual news items. By integrating the news aggregation step into the networks for market prediction, MANA-Net allows for trainable sentiment representations that are optimized directly for prediction. We evaluate MANA-Net using the S&P 500 and NASDAQ 100 indices, along with financial news spanning from 2003 to 2018. Experimental results demonstrate that MANA-Net outperforms various recent market prediction methods, enhancing Profit & Loss by 1.1% and the daily Sharpe ratio by 0.252.
Trojan Activation Attack: Red-Teaming Large Language Models using Steering Vectors for Safety-Alignment
Authors: Haoran Wang, Kai Shu
Venue: CIKM
Year: 2024

To ensure AI safety, instruction-tuned Large Language Models (LLMs) are specifically trained to ensure alignment, which refers to making models behave in accordance with human intentions. While these models have demonstrated commendable results on various safety benchmarks, the vulnerability of their safety alignment has not been extensively studied. This is particularly troubling given the potential harm that LLMs can inflict. Existing attack methods on LLMs often rely on poisoned training data or the injection of malicious prompts. These approaches compromise the stealthiness and generalizability of the attacks, making them susceptible to detection. Additionally, these models often demand substantial computational resources for implementation, making them less practical for real-world applications. In this work, we study a different attack scenario, called Trojan Activation Attack (TA2), which injects trojan steering vectors into the activation layers of LLMs. These malicious steering vectors can be triggered at inference time to steer the models toward attacker-desired behaviors by manipulating their activations. Our experiment results on four primary alignment tasks show that TA2 is highly effective and adds little or no overhead to attack efficiency. Additionally, we discuss potential countermeasures against such activation attacks.
Relative Contrastive Learning for Sequential Recommendation with Similarity-based Positive Sample Selection
Authors: Zhikai Wang, Yanyan Shen, Zexi Zhang, Li He, Yichun Li, Hao Gu, Yinghua Zhang
Venue: CIKM
Year: 2024

Contrastive Learning (CL) enhances the training of sequential recommendation (SR) models through informative self-supervision signals. Existing methods often rely on data augmentation strategies to create positive samples and promote representation invariance. Some strategies such as item reordering and item substitution may inadvertently alter user intent. Supervised Contrastive Learning (SCL) based methods find an alternative to augmentation-based CL methods by selecting same-target sequences (interaction sequences with the same target item) to form positive samples. However, SCL-based methods suffer from the scarcity of same-target sequences and consequently lack enough signals for contrastive learning. In this work, we propose to use similar sequences (with different target items) as additional positive samples and introduce a Relative Contrastive Learning (RCL) framework for sequential recommendation. RCL comprises a dual-tiered positive sample selection module and a relative contrastive learning module. The former module selects same-target sequences as strong positive samples and selects similar sequences as weak positive samples. The latter module employs a weighted relative contrastive loss, ensuring that each sequence is represented closer to its strong positive samples than its weak positive samples. We apply RCL on two mainstream deep learning-based SR models, and our empirical results reveal that RCL can achieve 4.88% improvement averagely than the state-of-the-art SR methods on five public datasets and one private dataset. The code can be found at https://github.com/Cloudcatcher888/RCL.
Bots Shield Fake News: Adversarial Attack on User Engagement based Fake News Detection
Authors: Lanjun Wang, Zehao Wang, Le Wu, An-An Liu
Venue: CIKM
Year: 2024

The surge in detecting fake news on social networks leads to increased research attention, particularly in the realm of deep learning models based on graph neural networks (GNNs). However, as research progresses, concerns emerge about the vulnerability of these detection models. In this study, we introduce an attack problem that perturbs user-news engagements by injecting bots to shield the targeted fake news from being detected by GNN-based fake news detection models. We propose a black-box attack method named Query-enhanced Surrogate-based Attack under Assortativity Constraint (QSA-AC) to work for this attack problem. QSA-AC combines surrogate-based and query-based approaches to improve attack effectiveness. At the same time, QSA-AC maintains a balance between attack effectiveness and imperceptibility by adjusting the local fluctuations of the assortativity with respect to the news on the social network. In addition, we introduce an evaluation metric, local strength assortativity perturbation rate (LSAPR), to assess the imperceptibility of the attack from the local perspective. Extensive experiments on two fake news datasets demonstrate that the proposed QSA-AC can achieve the optimal attack effectiveness, and control the trade-off between the attack effectiveness and imperceptibility.
HC-GST: Heterophily-aware Distribution Consistency based Graph Self-training
Authors: Fali Wang, Tianxiang Zhao, Junjie Xu, Suhang Wang
Venue: CIKM
Year: 2024

Graph self-training (GST), which selects and assigns pseudo-labels to unlabeled nodes, is popular for tackling label sparsity in graphs. However, recent study on homophily graphs show that GST methods could introduce and amplify distribution shift between training and test nodes as they tend to assign pseudo-labels to nodes they are good at. As GNNs typically perform better on homophilic nodes, there could be potential shifts towards homophilic pseudo-nodes, which is underexplored. Our preliminary experiments on heterophilic graphs verify that these methods can cause shifts in homophily ratio distributions, leading to training bias that improves performance on homophilic nodes while degrading it on heterophilic ones. Therefore, we study a novel problem of reducing homophily ratio distribution shifts during self-training on heterophilic graphs. A key challenge is the accurate calculation of homophily ratios and their distributions without extensive labeled data. To tackle them, we propose a novel Heterophily-aware Distribution Consistency-based Graph Self-Training (HC-GST) framework, which estimates homophily ratios using soft labels and optimizes a selection vector to align pseudo-nodes with the global homophily ratio distribution. Extensive experiments on both homophilic and heterophilic graphs show that HC-GST effectively reduces training bias and enhances self-training performance.
Empowering Shoppers with Event-focused Search
Authors: Austin R. Ward, Omar Alonso
Venue: CIKM
Year: 2024

We present Event-focused Search, an automated and scalable pipeline designed to facilitate event discovery and enhance event-based search. This is done by leveraging large language models (LLMs) to populate event datasets, perform temporal search based on selected dates, and aggregate search results based on appropriate events based on those searches. We illustrate this pipeline through proof-of-concept interfaces in an e-commerce context, though such a framework is applicable to different types of search scenarios (e.g., sports, entertainment).
GAD: A Generalized Framework for Anomaly Detection at Different Risk Levels
Authors: Rulan Wei, Zewei He, Martin Pavlovski, Fang Zhou
Venue: CIKM
Year: 2024

Anomaly detection is a crucial data mining problem due to its extensive range of applications. In real-world scenarios, anomalies often exhibit different levels of priority. Unfortunately, existing methods tend to overlook this phenomenon and identify all types of anomalies into a single class. In this paper, we propose a generalized formulation of the anomaly detection problem, which covers not only the conventional anomaly detection task, but also the partial anomaly detection task that is focused on identifying target anomalies of primary interest while intentionally disregarding non-target (low-risk) anomalies. One of the challenges in addressing this problem is the overlap among normal instances and anomalies of different levels of priority, which may cause high false positive rates. Additionally, acquiring a sufficient quantity of all types of labeled non-target anomalies is not always feasible. For this purpose, we present a generalized anomaly detection framework flexible in addressing a broader range of anomaly detection scenarios. Employing a dual-center mechanism to handle relationships among normal instances, non-target anomalies, and target anomalies, the proposed framework significantly reduces the number of false positives caused by class overlap and tackles the challenge of limited amount of labeled data. Extensive experiments conducted on two publicly available datasets from different domains demonstrate the effectiveness, robustness and superior labeled data utilization of the proposed framework. When applied to a real-world application, it exhibits a lift of at least 7.08% in AUPRC compared to the alternatives, showcasing its remarkable practicality.
Process-Informed Deep Learning for Enhanced Order Fulfillment Cycle Time Prediction in On-Demand Grocery Retailing
Authors: Jiawen Wei, Ziwen Ye, Chuan Yang, Chen Chen, Guangrui Ma
Venue: CIKM
Year: 2024

Accurate prediction of Order Fulfillment Cycle Time (OFCT) is essential for improving customer satisfaction and operational efficiency within the domain of on-demand grocery retailing (OGR). OGR platforms typically rely on Front Distribution Centers (FDCs) to manage inventory and deploy dedicated fleets for last-mile delivery to fulfill customer demands. Orders are processed at FDCs initially and then dispatched to delivery fleets. OFCT is influenced by a multitude of factors such as order volume, processing capabilities, delivery capacities, and dispatching strategies. These factors pose significant challenges to refining OFCT prediction accuracy. This paper presents an innovative deep learning model informed by a detailed comprehension of the order fulfillment process, with the objective of significantly enhancing OFCT prediction precision. We employ Recurrent Neural Network (RNN) blocks to dynamically evaluate the workload across processing and delivery stages. To address the interactions among orders and the impact of latent courier dynamics on order prioritization, we incorporate a suite of specialized attention modules into our framework. Our approach further employs Deep Bayesian Multi-Target Learning (DBMTL) to discern the sequential interactions between various stages of order fulfillment, thereby elucidating the influence of earlier stages on subsequent ones. Through online experiments on Meituan-Maicai, one of the biggest OGR platforms in China, our model demonstrates its superiority by outperforming well-acknowledged and advanced baselines. Furthermore, we assess the contributions of specific designs in our model through ablation studies. Our research presents a notable advancement in OFCT prediction, providing valuable insights for OGR platforms seeking to optimize their fulfillment operations and enhance customer experiences.
LLM-based Automated Web Retrieval and Text Classification of Food Sharing Initiatives
Authors: Hao Wu, Hyunji Cho, Anna R. Davies, Gareth J. F. Jones
Venue: CIKM
Year: 2024

Urban and peri-urban (UPU) food systems encounter challenges in sustainability and are fragile and vulnerable to shocks. Addressing these issues is one of the key drivers of food sharing initiatives (FSIs) which focus on collective acts around food across the food system. FSIs range from seed sharing and surplus food redistribution to community composting. We describe our development and deployment of web retrieval and content classification tools designed to provide automated mapping of FSIs at scale to populate databases of FSIs within cities. We present our novel automated system tailored for retrieving, identifying, categorizing and real-time monitoring of FSIs in over 200 European cities. Developed within the European CULTIVATE project, this system not only aids in comprehending the complex dynamics of the food sharing economy, but also enhances its visibility and operational efficiency. The automation of these processes plays a vital role in supporting the goals of the CULTIVATE project, notably in promoting sustainable food practices and resilient local food networks. Our system integrates web search using queries constructed automatically using domain-specific vocabulary resources with Large Language Model (LLM) query writing and classification methods. Experimental results using a collection of data derived from real online FSI content underscore the potential of digital automation to make significant contributions to innovative digital solutions to contemporary sustainability challenges. As such, the findings of this work pave the way for future research and implementation in similar contexts.
Identifying Contemporaneous and Lagged Dependence Structures by Promoting Sparsity in Continuous-time Neural Networks
Authors: Fan Wu, Woojin Cho, David Korotky, Sanghyun Hong, Donsub Rim, Noseong Park, Kookjin Lee
Venue: CIKM
Year: 2024

Continuous-time dynamics models, e.g., neural ordinary differential equations, enable accurate modeling of underlying dynamics in time-series data. However, employing neural networks for parameterizing dynamics makes it challenging for humans to identify dependence structures, especially in the presence of delayed effects. In consequence, these models are not an attractive option when capturing dependence carries more importance than accurate modeling, e.g., in tsunami forecasting. In this paper, we present a novel method for identifying dependence structures in continuous-time dynamics models. We take a two-step approach: (1) During training, we promote weight sparsity in the model's first layer during training. (2) We prune the sparse weights after training to identify dependence structures. In evaluation, we test our method in scenarios where the exact dependence-structures of time-series are known. Compared to baselines, our method is more effective in uncovering dependence structures in data even when there are delayed effects. Moreover, we evaluate our method to a real-world tsunami forecasting, where the exact dependence structures are unknown beforehand. Even in this challenging scenario, our method still effective learns physically-consistent dependence structures and achieves high accuracy in forecasting.
StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal Contrast
Authors: Yu Wu, Ting Dang, Dimitris Spathis, Hong Jia, Cecilia Mascolo
Venue: CIKM
Year: 2024

Contrastive learning (CL) has emerged as a promising approach for representation learning in time series data by embedding similar pairs closely while distancing dissimilar ones. However, existing CL methods often introduce false negative pairs (FNPs) by neglecting inherent characteristics and then randomly selecting distinct segments as dissimilar pairs, leading to erroneous representation learning, reduced model performance, and overall inefficiency. To address these issues, we systematically define and categorize FNPs in time series into semantic false negative pairs and temporal false negative pairs for the first time: the former arising from overlooking similarities in label categories, which correlates with similarities in non-stationarity and the latter from neglecting temporal proximity. Moreover, we introduce StatioCL, a novel CL framework that captures non-stationarity and temporal dependency to mitigate both FNPs and rectify the inaccuracies in learned representations. By interpreting and differentiating non-stationary states, which reflect the correlation between trends or temporal dynamics with underlying data patterns, StatioCL effectively captures the semantic characteristics and eliminates semantic FNPs. Simultaneously, StatioCL establishes fine-grained similarity levels based on temporal dependencies to capture varying temporal proximity between segments and to mitigate temporal FNPs. Evaluated on real-world benchmark time series classification datasets, StatioCL demonstrates a substantial improvement over state-of-the-art CL methods, achieving a 2.9% increase in Recall and a 19.2% reduction in FNPs. Most importantly, StatioCL also shows enhanced data efficiency and robustness against label scarcity.
Time-Sensitve Retrieval-Augmented Generation for Question Answering
Authors: Feifan Wu, Lingyuan Liu, Wentao He, Ziqi Liu, Zhiqiang Zhang, Haofen Wang, Meng Wang
Venue: CIKM
Year: 2024

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by accessing external data sources, offering a promising way to improve accuracy and reliability. Despite its potential, conventional retrievers encounter bias and flaws with time-sensitive queries. In this paper, a benchmark query dataset is constructed to retrieve documents containing time-evolving facts, and the results show that current embedding-based similarity-matching methods struggle to handle queries with explicit temporal constraints. Therefore, we propose a novel approach that integrates supervised contrastive learning with tailored negative sample pairs for temporal constraints to train the retriever of an RAG system, along with query-side fine-tuning and routing techniques. Experimental results show that our approach significantly enhances the retriever performance of time-sensitive queries while ensuring the effectiveness of general queries. We will make the code and dataset publicly available at https://github.com/suzhou-22/TS-Retriever.
Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge Distillation
Authors: Lirong Wu, Yunfan Liu, Haitao Lin, Yufei Huang, Stan Z. Li
Venue: CIKM
Year: 2024

To bridge the gaps between powerful Graph Neural Networks (GNNs) and lightweight Multi-Layer Perceptron (MLPs), GNN-to-MLP Knowledge Distillation (KD) proposes to distill knowledge from a well-trained teacher GNN into a student MLP. In this paper, we revisit the knowledge samples (nodes) in teacher GNNs from the perspective of hardness, and identify that hard sample distillation may be a major performance bottleneck of existing graph KD algorithms. The GNN-to-MLP KD involves two different types of hardness, one student-free knowledge hardness describing the inherent complexity of GNN knowledge, and the other student-dependent distillation hardness describing the difficulty of teacher-to-student distillation. However, most of the existing work focuses on only one of these aspects or regards them as one thing. This paper proposes a simple yet effective Hardness-aware GNN-to-MLP Distillation (HGMD) framework, which decouples the two hardnesses and estimates them using a non-parametric approach. Finally, two hardness-aware distillation schemes (i.e., HGMD-weight and HGMD-mixup) are further proposed to distill hardness-aware knowledge from teacher GNNs into the corresponding nodes of student MLPs. As non-parametric distillation, HGMD does not involve any additional learnable parameters beyond the student MLPs, but it still outperforms most of the state-of-the-art competitors. HGMD-mixup improves over the vanilla MLPs by 12.95% and outperforms its teacher GNNs by 2.48% averaged over seven real-world datasets. Codes will be made public at https://github.com/LirongWu/HGMD.
Bridge the Gap between Past and Future: Siamese Model Optimization for Context-Aware Document Ranking
Authors: Songhao Wu, Quan Tu, Mingjie Zhong, Hong Liu, Jia Xu, Jinjie Gu, Rui Yan
Venue: CIKM
Year: 2024

In the realm of information retrieval, users often engage in multi-turn interactions with search engines to acquire information, leading to the formation of sequences of user feedback behaviors. Leveraging the session context has proven to be beneficial for inferring user search intent and document ranking. A multitude of approaches have been proposed to exploit in-session context for improved document ranking. Despite these advances, the limitation of historical session data for capturing evolving user intent remains a challenge. In this work, we explore the integration of future contextual information into the session context to enhance document ranking. We present the siamese model optimization framework, comprising a history-conditioned model and a future-aware model. The former processes only the historical behavior sequence, while the latter integrates both historical and anticipated future behaviors. Both models are trained collaboratively using the supervised labels and pseudo labels predicted by the other. The history-conditioned model, referred to as ForeRanker, progressively learns future-relevant information to enhance ranking, while it singly uses historical session at inference time. To mitigate inconsistencies during training, we introduce the peer knowledge distillation method with a dynamic gating mechanism, allowing models to selectively incorporate contextual information. Experimental results on benchmark datasets demonstrate the effectiveness of our ForeRanker, showcasing its superior performance compared to existing methods.