OpenRaiser/Intern-Atlas huggingface dataset:
Hierarchical Structure Construction on Hypergraphs
Authors: Qi Luo, Wenjie Zhang, Zhengyi Yang, Dong Wen, Xiaoyang Wang, Dongxiao Yu, Xuemin Lin
Venue: CIKM
Year: 2024

Exploring the hierarchical structure of graphs presents notable advantages for graph analysis, revealing insights ranging from individual vertex behavior to community distribution and overall graph stability. This paper studies hierarchical structures within hypergraphs, where a hyperedge can connect multiple vertices. We observed that directly extending hierarchical frameworks from pairwise graphs to hypergraphs overlooks high-order interactions and can result in either high computational complexity or sparse hierarchy structure. To address this challenge, we introduce a dual-layer hypergraph hierarchy consisting of a primary hierarchy and a secondary hierarchy, enabling the construction of a refined hypergraph hierarchy in linear time. The dual-layer hierarchy establishes a global hierarchy based on vertex cohesion, utilizing vertex-induced subhypergraphs, and a local hierarchy based on hyperedge containment, employing edge-induced subhypergraphs. The combination of global and local hierarchy mitigates the homogeneity and sparsity issues inherent in single-layer hierarchies, allowing more effective modeling of high-order interactions. Furthermore, we propose an efficient hierarchical construction algorithm by leveraging a novel hyperedge-based disjoint set to identify connected subhypergraphs. Additionally, to optimize the local hierarchy further and prevent the emergence of excessively redundant levels, we introduce a compact local hierarchy by defining a restricted subgraph metric to eliminate redundancy caused by large-sized hyperedges. Empirical studies on real-world hypergraphs demonstrate the effectiveness of our approach.
A Knowledge-Enhanced Transformer-FL Method for Fault Root Cause Localization
Authors: Zhe Lv, Yaqiong Liu, Xidian Wang, Peng Gao, Zhouyuan Li, Yuanzhen Jiang
Venue: CIKM
Year: 2024

Root cause analysis for faults is one of the core tasks in the operation and maintenance of communication networks. Although artificial intelligence techniques can be used to assist manual inspections, fault diagnosis is still a tough problem. In the scenario of fault root cause localization, insufficient associated information makes it difficult to accurately determine the root cause. Meanwhile, it is a big challenge to extract as many feature details as possible from limited information and then fully utilize them. Therefore, this paper proposes a Knowledge-Enhanced Transformer-FL method, namely, KETrans-FL, to address the problem of root cause localization, by treating it as a multi-class classification problem. Our method first constructs a knowledge graph for knowledge enhancement, which consists of four types of nodes (base station, alarm, fault and alarm level) and their relationships based on the network operations. This knowledge enhancement technique incorporates real operation data and other external knowledge (e.g., alarm level) to serve as the source of feature information and thus can extract statistical and embedded features. Then, our method designs a Trans-FL (Transformer-Focal Loss) model, which uses the adapted Transformer encoder to learn the correlation information between input features to generate classification probabilities and employs Focal Loss as the loss function to mitigate severe class imbalance in the multi-class classification problem. Experimental results show that our proposed KETrans-FL method achieves a classification accuracy of nearly 91% and an average AUC score of 93%, indicating a significant improvement on fault root cause localization compared to baseline models. In addition, experimental results also validate the remarkable effect of our knowledge enhancement technique on improving the final classification accuracy.
Towards Advancing Text-Based User and Item Representation in Personalized Recommendation
Authors: Hanjia Lyu
Venue: CIKM
Year: 2024

In the realm of personalized recommendation systems, accurately capturing user preferences and item characteristics is important for delivering relevant and satisfying recommendations. This study introduces innovative approaches using Large Language Models (LLMs) to generate detailed textual descriptions that enhance both user and item representations. We propose a dual strategy: for user representation, we employ supervised fine-tuning coupled with Retrieval-Augmented Generation (RAG) to keep the model current with dynamic user preferences; for item representation, we leverage the extensive knowledge base of LLMs to enrich item descriptions and infer traits from user interactions. These methods promise a deeper, more nuanced understanding of both users and items, potentially leading to superior recommendation accuracy. We adopt a rigorous evaluation methodology, ensuring the reliability of our results and the effectiveness of our proposed system. This paper discusses these methodologies, presents our preliminary findings, and highlights the potential of text-augmented profiles in advancing recommendation systems.
3rd International Workshop on Industrial Recommendation Systems (IRS)
Authors: Luyi Ma, Xiaohan Li, Kamilia Ahmadi, Jianpeng Xu, Philip S. Yu, George Karypis
Venue: CIKM
Year: 2024

Recommendation systems are used widely across many industries, such as e-commerce, multimedia content platforms, and social networks, to provide suggestions that users will most likely consume or connect, thus improving the user experience. This motivates people in industry and research organizations to focus on personalization and recommendation algorithms, resulting in many research papers. While academic research mostly focuses on the performance of recommendation algorithms in terms of ranking quality or accuracy, it often neglects key factors that impact how a recommendation system will perform in a real-world environment, including but not limited to business metric definition and evaluation, scalability, recommendation quality control, robustness, fairness, and resource limitations, such as computing and memory resources budgets, engineering workforce cost, etc. The gap in constraints and requirements between academic research and industry limits the broad applicability of many of academia's contributions to industrial recommendation systems. This workshop aspires to bridge this gap by bringing together researchers from both academia and industry. Its goal is to serve as a venue for industrial researchers to share practical insights and for academic researchers to become aware of the additional factors of algorithm adoption in real production systems.
Unveiling Intellectual Property Vulnerabilities of GAN-Based Distributed Machine Learning through Model Extraction Attacks
Authors: Mengyao Ma, Shuofeng Liu, M. A. P. Chamikara, Mohan Baruwal Chhetri, Guangdong Bai
Venue: CIKM
Year: 2024

Generative Adversarial Networks (GANs), as a cornerstone of artificial intelligence (AI), are widely recognized as the intellectual property (IP) of their owners, given the sensitivity of the training data and the commercial value tied to the models. Model extraction attacks, which aim to steal well-trained proprietary models, pose a significant threat to model IP. Nevertheless, current research predominately focuses on the context of machine learning as a service (MLaaS), where the emphasis lies in understanding the attack knowledge acquired through black-box API queries. This restricted perspective exposes a critical gap in investigating model extraction attacks within realistic distributed settings for generative tasks. In this work, we present the first investigation into model extraction attacks against GANs in distributed settings. We provide a comprehensive attack taxonomy, considering three different levels of knowledge the adversary can obtain in practice. Based on it, we introduce a novel model extraction attack named MoEx, which focuses on the GAN-based distributed learning scenario, i.e., Multi-Discriminator GANs, a typical asymmetric distributed setting. MoEx uses the objective function simulation, leveraging data exchanged during the learning process, to approximate the GAN generator owned by the server. We define two attack goals for MoEx, fidelity extraction and accuracy extraction. Then we comprehensively evaluate the effectiveness of MoEx's two goals with real-world datasets. Our results demonstrate its robust capabilities in extracting generators with high fidelity and accuracy compared with existing methods.
An Explainable Multi-atlas Fusion Model based on Spatial Overlap for ASD Diagnosis
Authors: Yuefeng Ma, Xiaochen Mu, Tengfei Zhang
Venue: CIKM
Year: 2024

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental condition. Prompt recognition and treatment are vital for enhancing the life quality of individuals affected by ASD. However, current research either focus on a single atlas or a simple matrix concatenation combination, neglecting the complex and spatial relationship among the brain regions in different atlases. To tackle this weakness, in this paper, we propose a novel multi-atlas time-series feature fusion model with three steps based on spatial overlap proportion of brain regions to obtain an explainable representation of brain networks, which aims to achieve excellent diagnosis of ASD/TC. Specifically, we formally introduce the concept of spatial overlap and give its measurement, spatial overlap proportion. Then, we fuse the brain regions of multi-atlas to obtain an explainable brain networks of each subject. Finally, the GCN classifier is used to perform the final classification. The experimental results on Autism Brain Imaging Data Exchange (ABIDE) demonstrate that our proposed method achieved an accuracy of 0.771. Overall, our method outperforms SOTA methods in ASD/TC classification.
CH-Mits: A Cross-Modal Dataset for User Sentiment Analysis on Chinese Social Media
Authors: Juhao Ma, Shuai Xu, Yilin Liu, Xiaoming Fu
Venue: CIKM
Year: 2024

Multimodal social network user sentiment analysis aims to determine users' emotional polarity (positive or negative) by mining the associations between multiple data types such as images and texts. Existing public datasets are mainly constructed from English social media platforms, while Chinese social media datasets for multimodal user sentiment analysis are extremely scarce. In terms of the posts published by Chinese social media users, it is not rare that the emotional polarity delivered by the image and the textual content is inconsistent. Given such emotional inconsistency between images and texts, how to effectively identify users' true emotion polarity is still challenging. Toward the above issues, in this paper, we firstly construct a Chinese social media dataset CH-Mits for multimodal user sentiment analysis. In order to evaluate the usability of the dataset, we conceive and implement a novel model called PEMNet, and compare it with state-of-the-art models based on the CH-Mits dataset. In the end, we analyze the performance of PEMNet on selected samples with emotional inconsistency between images and texts. The constructed dataset and codes for PEMNet are available at https://github.com/Marblrdumdore/CH-Mits.
Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced Recommendation
Authors: Jianxing Ma, Zhibo Xiao, Luwei Yang, Hansheng Xue, Xuanzhou Liu, Wen Jiang, Wei Ning, Guannan Zhang
Venue: CIKM
Year: 2024

To cater to users' desire for an immersive browsing experience, numerous e-commerce platforms provide various recommendation scenarios, with a focus on Trigger-Induced Recommendation (TIR) tasks. However, the majority of current TIR methods heavily rely on the trigger item to understand user intent, lacking a higher-level exploration and exploitation of user intent (e.g., popular items and complementary items), which may result in an overly convergent understanding of users' short-term intent and can be detrimental to users' long-term purchasing experiences. Moreover, users' short-term intent shows uncertainty and is affected by various factors such as browsing context and historical behaviors, which poses challenges to user intent modeling. To address these challenges, we propose a novel model called Deep Uncertainty Intent Network (DUIN), comprising three essential modules: i) Explicit Intent Exploit Module extracting explicit user intent using the contrastive learning paradigm; ii) Latent Intent Explore Module exploring latent user intent by leveraging the multi-view relationships between items; iii) Intent Uncertainty Measurement Module offering a distributional estimation and capturing the uncertainty associated with user intent. Experiments on three real-world datasets demonstrate the superior performance of DUIN compared to existing baselines. Notably, DUIN has been deployed across all TIR scenarios in our e-commerce platform, with online A/B testing results conclusively validating its superiority.
Ask or Recommend: An Empirical Study on Conversational Product Search
Authors: Heli Ma, Jie Zou, Mohammad Aliannejadi, Evangelos Kanoulas, Yi Bin, Yang Yang
Venue: CIKM
Year: 2024

Conversational Product Search (CPS) provides an engaging way for users to find products through effective natural language conversations. However, understanding the effect of conversational characteristics on user search performance and when to ask clarifying questions or recommend products remains unexplored. To fill the gap, we conduct an empirical study in this paper. Specifically, we developed a conversational system that allows participants to join as customers or shopping assistants, to simulate the conversational product search activity. Data collected from conversations and participant feedback indicate that: (a) CPS systems tend to ask clarifying questions early in the conversation when users express the intent of issuing a new query and chitchat, while they tend to recommend products at a later stage of conversations; asking clarifying questions early and recommending products lately can significantly improve search performance and user's satisfaction; (b) asking clarifying questions and more fine-grained search keywords positively influence search performance in terms of finding relevant products; (c) although the conversation time has a positive impact on the number of recommended products, the performance gain diminishes with longer conversation time; (d) more clarifying questions, more conversation turns, and longer system response time lead to decreased user satisfaction.
Combat Greenwashing with GoalSpotter: Automatic Sustainability Objective Detection in Heterogeneous Reports
Authors: Mohammad Mahdavi, Ramin Baghaei Mehr, Tom Debus
Venue: CIKM
Year: 2024

Sustainable development is nowadays a prominent factor for the public. As a result, companies publish their sustainability visions and strategies in various reports to show their commitment to saving the environment and promoting social progress. However, not all statements in these sustainability reports are fact-based. When a company tries to mislead the public with its non-fact-based sustainability claims, greenwashing happens. To combat greenwashing, society needs effective automated approaches to identify the sustainability claims of companies in their heterogeneous reports. In this paper, we present a new sustainability objective detection system, named GoalSpotter, that automatically identifies the environmental and social claims of companies in their heterogeneous reports. Our system extracts text blocks of diverse reports, preprocesses and labels them using domain expert annotations, and then fine-tunes transformer models on the labeled text blocks. This way, our system can detect sustainability objectives in any new heterogeneous report. As our experiments show, our system outperforms existing state-of-the-art sustainability objective detection approaches. Furthermore, our post-deployment results show the significant impacts of our system in real-world business.
ToxVI: a Multimodal LLM-based Framework for Generating Intervention in Toxic Code-Mixed Videos
Authors: Krishanu Maity, A. S. Poornash, Sriparna Saha, Kitsuchart Pasupa
Venue: CIKM
Year: 2024

While considerable research has delved into detecting toxic content in text-based data, the realm of video content, particularly in languages other than English, has received less attention. Prior studies have primarily focused on creating automated tools to identify online toxic speech but have often overlooked the crucial next steps of mitigating its impact and discouraging future use. We can discourage social media users from sharing such material by automatically generating interventions that explain why certain content is inappropriate. To bridge this research gap, we propose an innovative task: generating interventions for toxic videos in code-mixed languages which go beyond existing methods focusing on text and images to combat online toxicity. We are introducing a Toxic Code-Mixed Intervention Video benchmark dataset (ToxCMI), comprising 1697 code-mixed toxic video utterances sourced from YouTube. Each utterance in this dataset has been meticulously annotated for toxicity and severity, accompanied by interventions provided in Hindi-English code-mixed languages. We have developed an advanced multimodal framework ToxVI, specifically designed for the task of generating Toxic Video appropriate Interventions, leveraging Large Language Models (LLMs), which comprises three modules - Modality module, Cross-Modal Synchronization module and Generation module. Our experiments demonstrate that integrating multiple modalities from the videos significantly enhances the performance of the proposed task and outperforms all the baselines by a significant margin.
Multi-view Causal Graph Fusion Based Anomaly Detection in Cyber-Physical Infrastructures
Authors: Arun Vignesh Malarkkan, Dongjie Wang, Yanjie Fu
Venue: CIKM
Year: 2024

The rise in cyber attacks on cyber-physical critical infrastructures, like water treatment networks, is evidenced by the growing frequency of breaches and the evolving sophistication of attack methods. Attack detection in such vulnerable critical infrastructures can be generalized into a task of anomaly detection with multivariate stream data. There are two essential challenges of this task: 1) Evolving and Shifting data streams; and 2) Robust Attack Pattern representation. Existing anomaly detection approaches, including statistical, distance, density, neural network, and graph-based methods, are not specialized in solving the spurious statistical relationships of evolving distribution shifts in sensing data streams. To address the two challenges, we propose a multi-view causal graph perspective, where 1) We build causal graphs to capture invariant anomaly patterns in varying streams; and 2) Introduce multi-view fusion for robust attack pattern representation. To implement this technical perspective, we develop a fused multi-view causal graph-aware anomaly detection framework. This framework includes two phases: 1) Multi-view Causal Graphs and Spectral Fusion, where we learn the dense view and sparse view causal graphs from sensory data streams and fuse the two causal graphs into a single weighted Laplacian matrix representation. 2) Graph Anomaly Detection, where we train a Deep Convolutional Graph Neural Network (DGCNN) on the Laplacian representation of the "Attack" and "Normal" status graphs to detect attack statuses on sensory data streams per time interval. Our framework achieves a ROC-Score of 82.4% and 93.2% on the SWaT and WADI Water Treatment Network Datasets with an improvement of 9.03% and 16.5% on the f1-score respectively when compared with the best-performing baseline methods on both the datasets.
Building Natural Language Interface for Product Search
Authors: Vijit Malik, Vinayak Puranik, Anirban Majumder, Vivek Sembium
Venue: CIKM
Year: 2024

Automatic extraction of attribute preferences from search queries is a critical problem in providing accurate product recommendations to customer. The task becomes even more challenging in cold-start settings where we do not have any supervised/labelled data available to train ML models. In this work, we implement a novel dataset generation pipeline (LLM-API) that leverages Large Language Models (LLMs), search logs and proprietary product information data from an ecommerce website to create a high quality dataset. Our proposed pipeline of LLM-API is robust as it can generalize to any product category with minimal changes in the LLM prompts. For the problem of converting product search queries to API calls we propose a multi-task schema generator model which we train on our generated dataset. Experiments on an internal test set reveals that our proposed model achieves an improvement of ≈9.6% and ≈5% in Exact Match and Micro-F1 respectively, over competitive baselines. Benchmarking our approach on public test set of search queries further reveals a gain of ≈8.6% and ≈10.5% in Exact Match and Micro-F1. We further demonstrate that our approach outperforms a state-of-the-art LLM (Claude) applied on our task using few-shot prompting and CoT reasoning, while at the same time, achieves improvement in inference latency.
Do We Really Need to Drop Items with Missing Modalities in Multimodal Recommendation?
Authors: Daniele Malitesta, Emanuele Rossi, Claudio Pomo, Tommaso Di Noia, Fragkiskos D. Malliaros
Venue: CIKM
Year: 2024

Generally, items with missing modalities are dropped in multimodal recommendation. However, with this work, we question this procedure, highlighting that it would further damage the pipeline of any multimodal recommender system. First, we show that the lack of (some) modalities is, in fact, a widely-diffused phenomenon in multimodal recommendation. Second, we propose a pipeline that imputes missing multimodal features in recommendation by leveraging traditional imputation strategies in machine learning. Then, given the graph structure of the recommendation data, we also propose three more effective imputation solutions that leverage the item-item co-purchase graph and the multimodal similarities of co-interacted items. Our method can be plugged into any multimodal RSs in the literature working as an untrained pre-processing phase, showing (through extensive experiments) that any data pre-filtering is not only unnecessary but also harmful to the performance.
Quantifying Uncertainty in Neural Networks through Residuals
Authors: Dalavai Udbhav Mallanna, Rini Smita Thakur, Rajeev Ranjan Dwivedi, Vinod K. Kurmi
Venue: CIKM
Year: 2024

Regression models are of fundamental importance in explicitly explaining the response variable in terms of covariates. However, point predictions of these models limit them from many real world applications. Heteroscedasticity is common in most real-world scenarios and is hard to model due to its randomness. The Gaussian process generally captures epistemic (model) uncertainty but fails to capture heteroscedastic aleatoric uncertainty. The framework of HetGP inherently captures both epistemic and aleatoric by placing independent GP's priors on both mean function and error term. We propose the posthoc HetGP on the residuals of the trained deterministic neural network to obtain both epistemic and aleatoric uncertainty. The advantage of posthoc HetGP on residuals is that it can be extended to any type of model, since the model is assumed to be black-box that gives point predictions. We demonstrate our approach through simulation studies and UCI regression datasets. The code is available at https://visdomlab.github.io/HetGP/