OpenRaiser/Intern-Atlas huggingface dataset:
Workshop on Generative AI for E-commerce
Authors: Mansi Ranjit Mane, Djordje Gligorijevic, Dingxian Wang, Behzad Shahrasbi, Topojoy Biswas, Evren Körpeoglu, Marios Savvides
Venue: CIKM
Year: 2024

The "Gen AI for E-commerce" workshop explores the role of Generative Artificial Intelligence in transforming e-commerce through enhanced user experience and operational efficiency. E-commerce companies grapple with multiple challenges such as lack of quality content for products, subpar user experience, sparse datasets etc. Gen AI offers significant potential to address these complexities. Yet, deploying these technologies at scale presents challenges such as hallucination in data, excessive costs, increased latency response, and limited generalization in sparse data environments. This workshop will bring together experts from academia and industry to discuss these challenges and opportunities, aiming to showcase case studies, breakthroughs, and insights into practical implementations of Gen AI in e-commerce.
Data Void Exploits: Tracking & Mitigation Strategies
Authors: Miro Mannino, Junior Garcia, Reem Hazim, Azza Abouzied, Paolo Papotti
Venue: CIKM
Year: 2024

A data void is a gap in online information, providing an opportunity for the spread of disinformation or a data void exploit. We introduce lightweight measures to track the progress of data void exploits and mitigation efforts in two contexts: Web search and Knowledge Graph (KG) querying. We use case studies to demonstrate the viability of these measures as data void trackers in the Web search context. To tackle data voids, we introduce an adversarial game model involving two agents: a disinformer and a mitigator. Both agents insert content into the information ecosystem to have their narrative rank higher than their counterpart in search results. At every turn, each agent chooses which content to deploy within their resource constraints, mimicking real-world situations where different entities have varying levels of influence and access to resources. Using simulations of this game, we compare and evaluate different mitigation strategies to recommend ones that maximize mitigation impact while minimizing costs.
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits
Authors: Masoud Mansoury, Bamshad Mobasher, Herke van Hoof
Venue: CIKM
Year: 2024

Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This bias becomes particularly problematic over time as a few items are repeatedly over-represented in recommendation lists, leading to a feedback loop that further amplifies this bias. Although extensive research has addressed this issue in model-based or neighborhood-based recommendation algorithms, less attention has been paid to online recommendation models, such as those based on top-K contextual bandits, where recommendation models are dynamically updated with ongoing user feedback. In this paper, we study exposure bias in a class of well-known contextual bandit algorithms known as Linear Cascading Bandits,. We analyze these algorithms in their ability to handle exposure bias and provide a fair representation of items in the recommendation results. Our analysis reveals that these algorithms fail to mitigate exposure bias in the long run during the course of ongoing user interactions. We propose an Exposure-Aware reward model that updates the model parameters based on two factors: 1) implicit user feedback and 2) the position of the item in the recommendation list. The proposed model mitigates exposure bias by controlling the utility assigned to the items based on their exposure in the recommendation list. Our experiments with two real-world datasets show that our proposed reward model improves the exposure fairness of the linear cascading bandits over time while maintaining the recommendation accuracy. It also outperforms the current baselines. Finally, we prove a high probability upper regret bound for our proposed model, providing theoretical guarantees for its performance.
OpenTOS: Open-source System for Transfer Learning Bayesian Optimization
Authors: Peili Mao, Ke Li
Venue: CIKM
Year: 2024

In recent years, many studies successfully integrated transfer learning techniques to improve the performance of Bayesian optimization. However, these advanced methods have not been widely adopted in real-world applications due to their inherent complexity and challenges in re-implementation and reproducibility. In this work, we introduce OpenTOS, an open-source system designed for transfer learning in Bayesian optimization. OpenTOS introduces a new implementation paradigm for these methods, allowing users to build different algorithms by choosing algorithmic components, similar to assembling LEGO blocks. Additionally, OpenTOS provides robust data management for supporting transfer learning with data from various sources. We also developed a web interface that allows for interactive building, analysis, and visualization of the optimization process. Powered by LLM, this interface offers a conversational experience, allowing users to interact with the system through natural language dialogue. OpenTOS is available as open-source on https://github.com/COLA-Laboratory/TransOPTGitHub .
SOUP: A Unified Shopping Query Suggestion Framework to Optimize Language Model with User Preference
Authors: Xu Meng, Zhaohui Luo, Xinxin Wang, Wen Jiang, Wei Ning, Shuhan Qi
Venue: CIKM
Year: 2024

The shopping query suggestion offers personalized queries to users and plays a crucial role in search engines. However, existing shopping query suggestion methods suffer from poor task generalization and limited semantic comprehension problems. This paper presents a comprehensive framework for the shopping query suggestion that effectively addresses the shortcomings of existing approaches. Our proposed framework leverages a generative language model and fine-grained preference alignment to enhance semantic comprehension and improve the quality of generated queries. Our key contributions include the introduction of a personalized prompt set for diverse query suggestion tasks, the integration of interaction behavior time to capture user query interests, and the utilization of reinforcement learning techniques to align user preferences. Experimental results demonstrate enhancements in different scenarios. Our codes are available at https://github.com/1170300319/CIKM2024_SOUP.
Semantic Prototypes: Enhancing Transparency without Black Boxes
Authors: Orfeas Menis-Mastromichalakis, Giorgos Filandrianos, Jason Liartis, Edmund Dervakos, Giorgos Stamou
Venue: CIKM
Year: 2024

As machine learning (ML) models and datasets increase in complexity, the demand for methods that enhance explainability and interpretability becomes paramount. Prototypes, by encapsulating essential characteristics within data, offer insights that enable tactical decision-making and enhance transparency. Traditional prototype methods often rely on sub-symbolic raw data and opaque latent spaces, reducing explainability and increasing the risk of misinterpretations. This paper presents a novel framework that utilizes semantic descriptions to define prototypes and provide clear explanations, effectively addressing the shortcomings of conventional methods. Our approach leverages concept-based descriptions to cluster data on the semantic level, ensuring that prototypes not only represent underlying properties intuitively but are also straightforward to interpret. Our method simplifies the interpretative process and effectively bridges the gap between complex data structures and human cognitive processes, thereby enhancing transparency and fostering trust. Our approach outperforms existing widely-used prototype methods in facilitating human understanding and informativeness, as validated through a user survey.
AI-safe Autocompletion with RAG and Relevance Curation
Authors: Kilian Merkelbach, Ksenia Riabinova, Arnab Dutta
Venue: CIKM
Year: 2024

In search, autocomplete (AC) is an essential tool that provides suggestions for each keystroke, functioning well with token-based queries. However, it is challenging to handle at scale when input queries are conversational and semantically rich. Identifying relevant queries for sub-tokens requires efficient lookup strategies, real-time ranking, and relevance in the results. This work integrates Retrieval-Augmented Generation (RAG), AI safety, and relevance ranking to produce autocomplete suggestions for conversational queries in a production system. RAG-based responses ensure a high hit ratio for popular AC inputs and maintain a very low risk category by not triggering any critical AI safety concerns.
Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social Balance
Authors: Marco Minici, Federico Cinus, Francesco Bonchi, Giuseppe Manco
Venue: CIKM
Year: 2024

Signed Graph Neural Networks (SGNNs) have recently gained attention as an effective tool for several learning tasks on signed networks, i.e., graphs where edges have an associated polarity. One of these tasks is to predict the polarity of the links for which this information is missing, starting from the network structure and the other available polarities. However, when the available polarities are few and potentially noisy, such a task becomes challenging. In this work, we devise a semi-supervised learning framework that builds around the novel concept of multiscale social balance to improve the prediction of link polarities in settings characterized by limited data quantity and quality. Our model-agnostic approach can seamlessly integrate with any SGNN architecture, dynamically reweighting the importance of each data sample while making strategic use of the structural information from unlabeled edges combined with social balance theory. Empirical validation demonstrates that our approach outperforms established baseline models, effectively addressing the limitations imposed by noisy and sparse data. This result underlines the benefits of incorporating multiscale social balance into SGNNs, opening new avenues for robust and accurate predictions in signed network analysis.
Saliency Detection in Educational Videos: Analyzing the Performance of Current Models, Identifying Limitations and Advancement Directions
Authors: Evelyn Navarrete, Ralph Ewerth, Anett Hoppe
Venue: CIKM
Year: 2024

Identifying the regions of a learning resource that a learner pays attention to is crucial for assessing the material's impact and improving its design and related support systems. Saliency detection in videos addresses the automatic recognition of attention-drawing regions in single frames. In educational settings, the recognition of pertinent regions in a video's visual stream can enhance content accessibility and information retrieval tasks such as video segmentation, navigation, and summarization. Such advancements can pave the way for the development of advanced AI-assisted technologies that support learning with greater efficacy. However, this task becomes particularly challenging for educational videos due to the combination of unique characteristics such as text, voice, illustrations, animations, and more. To the best of our knowledge, there is currently no study that evaluates saliency detection approaches in educational videos. In this paper, we address this gap by evaluating four state-of-the-art saliency detection approaches for educational videos. We reproduce the original studies and explore the replication capabilities for general-purpose (non-educational) datasets. Then, we investigate the generalization capabilities of the models and evaluate their performance on educational videos. We conduct a comprehensive analysis to identify common failure scenarios and possible areas of improvement. Our experimental results show that educational videos remain a challenging context for generic video saliency detection models.
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices
Authors: Thao Nguyen, Tiara Torres-Flores, Changhyun Hwang, Carl Edwards, Ying Diao, Heng Ji
Venue: CIKM
Year: 2024

This paper presents a novel approach for predicting Power Conversion Efficiency (PCE) of Organic Photovoltaic (OPV) devices, called GLaD: synergizing molecular Graphs and Language Descriptors for enhanced PCE prediction. Due to the lack of high-quality experimental data, we collect a dataset consisting of 500 pairs of OPV donor and acceptor molecules along with their corresponding PCE values, which we utilize as the training data for our predictive model. In this low-data regime, GLaD leverages properties learned from large language models (LLMs) pretrained on extensive scientific literature to enrich molecular structural representations, allowing for a multimodal representation of molecules. GLaD achieves precise predictions of PCE, thereby facilitating the synthesis of new OPV molecules with improved efficiency. Furthermore, GLaD showcases versatility, as it applies to a range of molecular property prediction tasks (BBBP, BACE, ClinTox and SIDER [45]), not limited to those concerning OPV materials. Especially, GLaD proves valuable for tasks in low-data regimes within the chemical space, as it enriches molecular representations by incorporating molecular property descriptions learned from large-scale pretraining. This capability is significant in real-world scientific endeavors like drug and material discovery, where access to comprehensive data is crucial for informed decision-making and efficient exploration of the chemical space.
Reliable Knowledge Graph Reasoning with Uncertainty Quantification
Authors: Bo Ni
Venue: CIKM
Year: 2024

Recently, Knowledge Graphs (KGs) have been successfully coupled with Large Language Models (LLMs) to mitigate their hallucinations and enhance their reasoning capability, e.g., KG-based retrieval-augmented framework for question-answering. However, current KG-LLM frameworks lack rigorous uncertainty estimation, limiting their reliable deployment in high-stake applications where the cost of errors is significant. To address this crucial gap, we propose a new trustworthy KG-LLM framework, UaG(Uncertainty Aware Graph Reasoning), which incorporates uncertainty quantification into the KG-LLM framework. We design an uncertainty-aware multi-step reasoning framework that leverages conformal prediction to provide a theoretical guarantee on the prediction set. To manage the error rate of the multi-step process, we additionally introduce an error rate control module to adjust the error rate within the individual components. Our preliminary results demonstrate that UaG can achieve the desired theoretical coverage while maintaining a reasonable prediction set size.
Exploring Robustness of GNN against Universal Injection Attack from a Worst-case Perspective
Authors: Dandan Ni, Sheng Zhang, Cong Deng, Han Liu, Gang Chen, Minhao Cheng, Hongyang Chen
Venue: CIKM
Year: 2024

Recently, graph neural networks (GNNs) have demonstrated outstanding performance in fundamental tasks such as node classification and link prediction, as well as in specialized domains like recommendation systems, fraud detection, and drug discovery. However, their vulnerability to adversarial attacks raises concerns about their reliability in security-critical areas. To address this issue, researchers are exploring various defense methods, including specific attack countermeasures and certifiable robustness approaches. Nevertheless, these strategies are often effective only against limited attack scenarios, and prevailing certification methods prove inadequate when confronted with injection attacks. In this paper, we propose a method named CERT_UIA to enhance the robustness of GNN models against worst-case attacks, specifically targeting the scenario of Universal node Injection Attacks (UIA), thereby filling a gap in the existing literature on certified robustness in this context. Our approach involves a two-stage attack process that replaces the transformations of the topology and feature spaces with equivalent unified feature transformations, unifying the optimization of worst-case perturbations into a single feature space. Furthermore, we empirically evaluate our method on several benchmark datasets and compare it with existing certified methods.
CADIF-OSN: Detecting Cloned Accounts with Missing Profile Attributes on Online Social Networks
Authors: Dewei Ning, Yong-Feng Ge, Hua Wang, Changjun Zhou
Venue: CIKM
Year: 2024

The growth of online social networks (OSNs) has become increasingly significant. Potential cloned accounts on these platforms raise serious concerns due to the risks they pose to user privacy and security. Previous works in the detection of cloned accounts on OSNs do not yield satisfactory results and lack consideration of the impact of missing attributes on the detection process. We propose cloned account detection with imputation framework for online social networks (CADIF-OSN) to accurately find potential cloned accounts on OSNs. This framework enables the accurate identification of potential cloned accounts on OSNs by leveraging their public profile information, even in cases where some of the information may not be accessible. The framework comprises four key components: 1) Fuzzy string matching with Levenshtein Distance that quickly generates suspicious account pairs by matching all the accounts' usernames and screennames; 2) An embedded method Doc2Vec that transforms all existing profile information of accounts into estimable vectors; 3) A HyperImpute model that imputes the missing information; and 4) A deep-forest model that is trained to detect cloned accounts. We evaluated our framework using a Twitter dataset consisting of 3,826 pairs of cloned accounts and 70,000 normal accounts. The evaluation results demonstrate that our framework significantly surpasses existing approaches in terms of Precision and F1-score.
Towards Better Seach Query Classification with Distribution-Diverse Multi-Expert Knowledge Distillation in JD Ads Search
Authors: Kun-Peng Ning, Ming Pang, Zheng Fang, Xue Jiang, Xi-Wei Zhao, Changping Peng, Zhangang Lin, Jinghe Hu, Jingping Shao, Li Yuan
Venue: CIKM
Year: 2024

In the dynamic landscape of online advertising, decoding user intent remains a pivotal challenge, particularly in the context of query classification. Swift classification models, exemplified by FastText, cater to the demand for real-time responses but encounter limitations in handling intricate queries. Conversely, accuracy-centric models like BERT introduce challenges associated with increased latency. This paper undertakes a nuanced exploration, navigating the delicate balance between efficiency and accuracy. It unveils FastText's latent potential as an 'online dictionary' for historical queries while harnessing the semantic robustness of BERT for novel and complex scenarios. The proposed Distribution-Diverse Multi-Expert (DDME) framework employs multiple teacher models trained from diverse data distributions. Through meticulous data categorization and enrichment, it elevates the classification performance across the query spectrum. Empirical results within the JD ads search system validate the superiority of our proposed approaches.
Extended Japanese Commonsense Morality Dataset with Masked Token and Label Enhancement
Authors: Takumi Ohashi, Tsubasa Nakagawa, Hitoshi Iyatomi
Venue: CIKM
Year: 2024

Rapid advancements in artificial intelligence (AI) have made it crucial to integrate moral reasoning into AI systems. However, existing models and datasets often overlook regional and cultural differences. To address this shortcoming, we have expanded the JCommonsenseMorality (JCM) dataset, the only publicly available dataset focused on Japanese morality. The Extended JCM (eJCM) has grown from the original 13,975 sentences to 31,184 sentences using our proposed sentence expansion method called Masked Token and Label Enhancement (MTLE). MTLE selectively masks important parts of sentences related to moral judgment and replaces them with alternative expressions generated by a large language model (LLM), while re-assigning appropriate labels. The model trained using our eJCM achieved an F1 score of 0.857, higher than the scores for the original JCM (0.837), ChatGPT one-shot classification (0.841), and data augmented using AugGPT, a state-of-the-art augmentation method (0.850). Specifically, in complex moral reasoning tasks unique to Japanese culture, the model trained with eJCM showed a significant improvement in performance (increasing from 0.681 to 0.756) and achieved a performance close to that of GPT-4 Turbo (0.787). These results demonstrate the validity of the eJCM dataset and the importance of developing models and datasets that consider the cultural context.
Ericsogate: Advancing Analytics and Management of Data from Diverse Sources within Ericsson Using Knowledge Graphs
Authors: Abdelghny Orogat, Sri Lakshmi Vadlamani, Dimple Thomas, Ahmed El-Roby
Venue: CIKM
Year: 2024

As data in the telecommunications industry becomes more voluminous and complex, extracting insightful information requires efficient and scalable systems that can effectively link and manage this data. This paper introduces a novel, multi-layered approach to managing interlinked data for Cloud Radio Access Network (CloudRAN) at Ericsson, utilizing Knowledge Graphs (KGs). Our system is structured into six distinct layers, each focusing on a specific aspect of managing interlinked data. This division enhances clarity and manageability, and promotes effective teamwork and collaborative development. A cornerstone of our architecture is its modularity, which enables the flexible exchange of components, such as the triple store, with minimal impact on the system's operations, ensuring longevity and adaptability to evolving technological trends. Moreover, we introduce novel applications in knowledge graph summarization and semantic search, specifically engineered for industrial decision-making. These innovations provide concise insights and actionable intelligence, fostering rapid and informed decision-making processes crucial for industry professionals. Finally, we discuss the lessons learned from deploying and utilizing this six-layer framework.