OpenRaiser/Intern-Atlas huggingface dataset:
ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings
Authors: Rehan Raza, Guanjin Wang, Kok Wai Wong, Hamid Laga, Marco Fisichella
Venue: CIKM
Year: 2025

Explainable Artificial Intelligence (XAI) methods, such as Local Interpretable Model-Agnostic Explanations (LIME), have advanced the interpretability of black-box machine learning models by approximating their behavior locally using interpretable surrogate models. However, LIME's inherent randomness in perturbation and sampling can lead to locality and instability issues, especially in scenarios with limited training data. In such cases, data scarcity can result in the generation of unrealistic variations and samples that deviate from the true data manifold. Consequently, the surrogate model may fail to accurately approximate the complex decision boundary of the original model. To address these challenges, we propose a novel Instance-based Transfer Learning LIME framework (ITL-LIME) that enhances explanation fidelity and stability in data-constrained environments. ITL-LIME introduces instance transfer learning into the LIME framework by leveraging relevant real instances from a related source domain to aid the explanation process in the target domain. Specifically, we employ clustering to partition the source domain into clusters with representative prototypes. Instead of generating random perturbations, our method retrieves pertinent real source instances from the source cluster whose prototype is most similar to the target instance. These are then combined with the target instance's neighboring real instances. To define a compact locality, we further construct a contrastive learning-based encoder as a weighting mechanism to assign weights to the instances from the combined set based on their proximity to the target instance. Finally, these weighted source and target instances are used to train the surrogate model for explanation purposes. Experimental evaluation with real-world datasets demonstrates that ITL-LIME greatly improves the stability and fidelity of LIME explanations in scenarios with limited data. Our code is available at https://github.com/rehanrazaa/ITL-LIME.
Fine-Grained Emotion Recognition via In-Context Learning
Authors: Zhaochun Ren, Zhou Yang, Chenglong Ye, Haizhou Sun, Chao Chen, Xiaofei Zhu, Xiangwen Liao
Venue: CIKM
Year: 2025

Fine-grained emotion recognition aims to identify the emotional type in queries through reasoning and decision-making processes, playing a crucial role in various systems. Recent methods use In-Context Learning (ICL), enhancing the representation of queries in the reasoning process through semantically similar examples, while further improving emotion recognition by explaining the reasoning mechanisms. However, these methods enhance the reasoning process but overlook the decision-making process. This paper investigates decision-making in fine-grained emotion recognition through prototype theory. We show that ICL relies on similarity matching between query representations and emotional prototypes within the model, where emotion-accurate representations are critical. However, semantically similar examples often introduce emotional discrepancies, hindering accurate representations and causing errors. To address this, we propose Emotion In-Context Learning (EICL), which introduces emotionally similar examples and uses a dynamic soft-label strategy to improve query representations in the emotion reasoning process. A two-stage exclusion strategy is then employed to assess similarity from multiple angles, further optimizing the decision-making process. Extensive experiments show that EICL significantly outperforms ICL on multiple datasets.
Modality Alignment with Multi-scale Bilateral Attention for Multimodal Recommendation
Authors: Kelin Ren, Chan-Yang Ju, Dong-Ho Lee
Venue: CIKM
Year: 2025

Multimodal recommendation systems are increasingly becoming foundational technologies for e-commerce and content platforms, enabling personalized services by jointly modeling users' historical behaviors and the multimodal features of items (e.g., visual and textual). However, most existing methods rely on either static fusion strategies or graph-based local interaction modeling, facing two critical limitations: (1) insufficient ability to model fine-grained cross-modal associations, leading to suboptimal fusion quality; and (2) a lack of global distribution-level consistency, causing representational bias. To address these, we propose MambaRec, a novel framework that integrates local feature alignment and global distribution regularization via attention-guided learning. At its core, we introduce the Dilated Refinement Attention Module (DREAM), which uses multi-scale dilated convolutions with channel-wise and spatial attention to align fine-grained semantic patterns between visual and textual modalities. This module captures hierarchical relationships and context-aware associations, improving cross-modal semantic modeling. Additionally, we apply Maximum Mean Discrepancy (MMD) and contrastive loss functions to constrain global modality alignment, enhancing semantic consistency. This dual regularization reduces mode-specific deviations and boosts robustness. To improve scalability, MambaRec employs a dimensionality reduction strategy to lower the computational cost of high-dimensional multimodal features. Extensive experiments on real-world e-commerce datasets show that MambaRec outperforms existing methods in fusion quality, generalization, and efficiency. Our code has been made publicly available at https://github.com/rkl71/MambaRec.
LLM-Driven Attributes Extraction in eCommerce
Authors: Ksenia Riabinova, Kilian Merkelbach
Venue: CIKM
Year: 2025

Aspect extraction - the task of identifying attributes such as model, color, or size from textual entities like question-answer pairs - is one of the key tasks in eCommerce. Given the large number of possible aspects per entity, retrieving the most relevant ones is challenging. Traditional methods rely on high-quality labeled data for training, which is costly to obtain at scale. In this work, we propose a training-free aspect extraction approach using LLMs. Leveraging in-context learning and a novel Forward-Backward method that combines retrieval-augmented generation (RAG) with embedding-based matching, our method effectively extracts relevant aspects from text without requiring training data.
Quantum-Amplitude Embedded Adaptation for Parameter-Efficient Fine-Tuning in Large Language Models
Authors: Emily Jimin Roh, Joongheon Kim
Venue: CIKM
Year: 2025

Large language models (LLMs) require substantial resources for task-specific adaptation, that motivates the development of parameter-efficient fine-tuning (PEFT) methods. This paper presents quantum-amplitude embedded adaptation (QAA), a novel PEFT framework that logarithmically compresses activation vectors using quantum-amplitude embedding and applies expressive non-linear transformations via parameterized quantum circuits (PQCs). By replacing linear adapters in attention modules with compact quantum modules, QAA achieves high expressivity while drastically reducing the number of trainable parameters. Empirical results demonstrate that QAA performs on par with or better than existing PEFT under constrained memory and compute budgets, highlighting its potential for efficient LLM fine-tuning.
Recommender Systems for Sustainable Development through Responsible Nudging
Authors: Mehrdad Rostami, Alexander Felfernig, Wolfgang Wörndl, Mourad Oussalah, Avishek Anand, Mahdi Jalili, Ashmi Banerjee
Venue: CIKM
Year: 2025

Recommender Systems (RS) influence everyday decisions, yet most remain optimized for short-term engagement or commercial gain. RS4SD aims to shift this focus by exploring how RS can contribute to sustainable development through behavioral change and nudging strategies. Aligned with the UN Sustainable Development Goals (SDG), RS4SD will highlight applications that promote responsible consumption, sustainable mobility, healthy eating, and digital well-being. In particular, we will focus on how AI and RS can be designed to foster sustainable behaviors through multi-objective optimization and ethically aligned interventions. These objectives are directly tied to the UN SDG, and we welcome all contributions showcasing RS in support of these goals. A central theme of the workshop is the integration of behavioral science and AI to design interventions that guide users toward more sustainable and healthier choices while preserving individual autonomy. Topics of interest include multi-objective recommendation, health-aware RS, eco-friendly product and tourism RS, as well as novel evaluation metrics that go beyond accuracy to capture societal impact. RS4SD will bring together researchers, stakeholders and practitioners from RS, AI, sustainability, and behavioral science to share models, datasets, frameworks, and real-world use cases. The workshop encourages interdisciplinary collaboration and aims to build a community dedicated to responsible, behavior-aware RS that benefit both individuals and society.
Eliminating Bias from Presentation Attack Detection Algorithms for Face Recognition Systems
Authors: Katarzyna Roszczewska
Venue: CIKM
Year: 2025

This paper presents an analysis of the fairness of presentation attack detection algorithms in face recognition systems. The study is the initial part of my Ph.D. research, with the aim of developing robust and unbiased PAD methodologies. One of the main aspects of this work involves the manual annotation of demographic groups within used biometric datasets, as tested automated tools do not offer sufficient accuracy for such labeling tasks. Another part of this work was the evaluation of two open-source PAD algorithms. These experiments reveal performance disparities across different demographic groups, highlighting the presence of algorithmic bias. Future stages of my research will explore the impact of sensor characteristics, specifically near-infrared (NIR) imaging, on demographic bias, with the ultimate goal of designing a bias-resilient PAD algorithm.
Retrieval of Graph Structured Objects: Theory and Applications
Authors: Indradyumna Roy, Soumen Chakrabarti, Abir De
Venue: CIKM
Year: 2025

Graph-structured data is ubiquitous across diverse domains like social networks, search, question answering, and drug discovery. Effective retrieval of (sub-)graphs with relevant substructures has become critical to the success of these applications. This proposed tutorial will introduce attendees to state-of-the-art neural methods for graph retrieval, highlighting architectures that effectively model relevance through innovative combinations of early and late interaction mechanisms. Participants will explore relevance models that represent graphs as sets of embeddings, enabling alignment-driven similarity scoring between query and corpus graphs and supporting diverse cost functions, both symmetric and asymmetric. We will also discuss compatibility with Approximate Nearest Neighbor (ANN) methods, covering recent advances in locality-sensitive hashing (LSH) and other indexing techniques that significantly enhance scalability in graph retrieval. The tutorial includes hands-on experience with an accessible, PyTorch-integrated toolkit that provides downloadable graph retrieval datasets and baseline implementations of recent methods. Participants will learn to adapt these methods for multi-modal applications --- such as molecule, text, and image retrieval --- where graph-based retrieval proves particularly effective. Designed for researchers and practitioners, this session delivers both foundational concepts and practical tools for implementing and scaling neural graph retrieval solutions across interdisciplinary applications.
PEQQS: a Dataset for Probing Extractive Quantity-focused Question Answering from Scientific Literature
Authors: Maciej Rybinski, Necva Bölücü, Huichen Yang, Stephen Wan
Venue: CIKM
Year: 2025

Question Answering (QA) and Information Retrieval (IR) play a crucial role in information-seeking pipelines implemented in many emerging AI research assistant applications. Large Language Models (LLMs) have demonstrated exceptional effectiveness on QA tasks, with Retrieval Augmented Generation (RAG) techniques often boosting the results. However, in many of those emerging applications, the onus of conducting the actual literature search falls on the user, i.e. the user searches for the relevant literature and the LLM-based assistant extracts the solicited answers from each of the user-supplied documents. The interplay between the quality of the user-conducted search and the quality of the final results remains understudied. In this work, we focus on a specific version of such pipeline, where users aim to obtain a specific quantity as an extractive answer (e.g., a value of a particular measurable parameter). To this end, we provide a dataset of 1031 agricultural sciences abstracts annotated with correct extractive answers. Additionally, this dataset builds on our previous work, which focused on quantity-centric search from a corpus of over 3.3M documents, which means the dataset also consists of 1104 query-document relevance judgments for 39 queries. The availability of both document-level annotations and corpus-level relevance judgments means that our dataset allows for an end-to-end evaluation of an information-seeking pipeline consisting of both literature search and the QA module. We present how our dataset can be used both for the evaluation of extractive quantity-focused QA from science literature and for exploring the impact of search on the downstream results, specifically focusing on hallucinations resulting from processing non-relevant documents with LLMs.
Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Authors: Seongeun Ryu, Yunyong Ko, Sang-Wook Kim
Venue: CIKM
Year: 2025

Personalized news recommendation aims to deliver news articles aligned with users' interests, serving as a key solution to alleviate the problem of information overload on online news platforms. While prior work has improved interest matching through refined representations of news and users, the following time-related challenges remain underexplored: (C1) leveraging the age of clicked news to infer users' interest persistence, and (C2) modeling the varying lifetime of news across topics and users. To jointly address these challenges, we propose a novel Lifetime-aware Interest Matching framework for nEws recommendation, named LIME, which incorporates three key strategies: (1) User-Topic lifetime-aware age representation to capture the relative age of news with respect to a user-topic pair, (2) Candidate-aware lifetime attention for generating temporally aligned user representation, and (3) Freshness-guided interest refinement for prioritizing valid candidate news at prediction time. Extensive experiments on two real-world datasets demonstrate that LIME consistently outperforms a wide range of state-of-the-art news recommendation methods, and its model-agnostic strategies significantly improve recommendation accuracy.
Empirical Study of Over-Squashing in GNNs and Causal Estimation of Rewiring Strategies
Authors: Danial Saber, Amirali Salehi-Abari
Venue: CIKM
Year: 2025

Graph neural networks (GNNs) have exhibited state-of-the-art performance across a wide range of domains. Yet message-passing GNNs suffer from over-squashing---exponential compression of long-range information from distant nodes---which limits expressivity. Rewiring techniques can ease this bottleneck, but their practical impacts are unclear due to the lack of a direct empirical over-squashing metric. We propose a topology-focused method for assessing over-squashing between node pairs using the decay rate of their mutual sensitivity. We then extend these pairwise assessments to graph-level statistics. Coupling these metrics with a within-graph causal design, we quantify how rewiring strategies affect over-squashing on diverse graph- and node-classification benchmarks. Our extensive empirical analyses show that most graph classification datasets suffer from over-squashing (but to various extents), and rewiring effectively mitigates it---though the degree of mitigation, and its translation into performance gains, varies by dataset and method. We also found that over-squashing is less notable in node classification datasets, where rewiring often increases over-squashing, and performance variations are uncorrelated with over-squashing changes. These findings suggest that rewiring is most beneficial when over-squashing is both substantial and corrected with restraint---while overly aggressive rewiring, or rewiring applied to minimally over-squashed graphs, is unlikely to help and may even harm performance. Our plug-and-play diagnostic tool lets practitioners decide whether rewiring is likely to pay off.
On Verifiable Legal Reasoning: A Multi-Agent Framework with Formalized Knowledge Representations
Authors: Albert Sadowski, Jaroslaw A. Chudziak
Venue: CIKM
Year: 2025

Legal reasoning requires both precise interpretation of statutory language and consistent application of complex rules, presenting significant challenges for AI systems. This paper introduces a modular multi-agent framework that decomposes legal reasoning into distinct knowledge acquisition and application stages. In the first stage, specialized agents extract legal concepts and formalize rules to create verifiable intermediate representations of statutes. The second stage applies this knowledge to specific cases through three steps: analyzing queries to map case facts onto the ontology schema, performing symbolic inference to derive logically entailed conclusions, and generating final answers using a programmatic implementation that operationalizes the ontological knowledge. This bridging of natural language understanding with symbolic reasoning provides explicit and verifiable inspection points, significantly enhancing transparency compared to end-to-end approaches. Evaluation on statutory tax calculation tasks demonstrates substantial improvements, with foundational models achieving 76.4% accuracy compared to 18.8% baseline performance, effectively narrowing the performance gap between reasoning and foundational models. These findings suggest that modular architectures with formalized knowledge representations can make sophisticated legal reasoning more accessible through computationally efficient models while enhancing consistency and explainability in AI legal reasoning, establishing a foundation for future research into more transparent, trustworthy, and effective AI systems for legal domain.
Neural Shifts in Collaborative Team Recommendation
Authors: Mahdis Saeedi, Hossein Fani
Venue: CIKM
Year: 2025

Team recommendation involves selecting skilled experts to form an almost surely successful collaborative team, or refining the team composition to maintain or excel at performance. To eschew the tedious and error-prone manual process, various computational and social science theoretical approaches have been proposed wherein the problem definition remains essentially the same, while it has been referred to by such other names as team allocation, selection, composition, and formation. In this tutorial, we study the advancement of computational approaches from greedy search in pioneering works to the recent learning-based approaches, with a particular in-depth exploration of graph neural network-based methods as the cutting-edge class, via unifying definitions, formulations, and evaluation schema. More importantly, we then discuss team refinement, a subproblem in team recommendation that involves structural adjustments or expert replacements to enhance team performance in dynamic environments. Finally, we introduce training strategies, benchmarking datasets, and open-source tools, along with future research directions and real-world applications. The tutorial artifacts can be found at https://fani-lab.github.io/OpeNTF/tutorial/cikm25.
A Use-Case Specific Dataset for Measuring Dimensions of Responsible Performance in LLM-generated Text
Authors: Alicia Sagae, Chia-Jung Lee, Sandeep Avula, Brandon Dang, Vanessa Murdock
Venue: CIKM
Year: 2025

Current methods for evaluating large language models (LLMs) typically focus on high-level tasks such as text generation, without targeting a particular AI application. This approach is not sufficient for evaluating LLMs for Responsible AI dimensions like fairness, since protected attributes that are highly relevant in one application may be less relevant in another. In this work, we construct a dataset that is driven by a real-world application (generate a plain-text product description, given a list of product features), parameterized by fairness attributes intersected with gendered adjectives and product categories, yielding a rich set of labeled prompts. We show how to use the data to identify quality, veracity, safety, and fairness gaps in LLMs, contributing a proposal for LLM evaluation paired with a concrete resource for the research community.
Green by Design: Detecting Environmental Claims in Corporate Web Content
Authors: Diya Saha, Manjira Sinha, Tirthankar Dasgupta
Venue: CIKM
Year: 2025

Corporate entities increasingly embed environmental claims in their digital communication to project sustainability awareness. Detecting such claims is critical for regulatory monitoring, corporate accountability, and mitigation of greenwashing practices. Traditional neural network architectures including large language models however, struggle to capture both the complex linguistic structures and the subtle stylistic cues that characterize environmental assertions. In this work, we propose a novel Graph-Augmented Liquid Neural Network (GLNN) architecture for automatic detection of environmental claims in corporate web content. Our approach first models the syntactic and semantic dependencies of text using a Graph Convolutional Network (GCN), while concurrently encoding stylistic features derived from linguistic markers (e.g., LIWC categories) into vector representations. These representations are concatenated and passed into a Liquid Time-Constant (LTC) Network, which provides dynamic adaptability and low-power efficiency by leveraging continuous-time recurrent dynamics. The integration of GCN-based stylistic encoding with LTC networks enables the model to robustly capture both structural dependencies and temporal signal variations inherent in corporate claims, while remaining energy efficient. Extensive experiments on multiple open datasets demonstrate that our model outperforms baseline neural architectures in both accuracy and computational efficiency, highlighting the potential of graph-augmented liquid networks as a foundation for sustainable AI in sustainability monitoring.
Open-Source LLM-based Relevance Assessment vs. Highly Reliable Manual Relevance Assessment: A Case Study
Authors: Tetsuya Sakai, Khant Myoe Rain, Rikiya Takehi, Sijie Tao, Young-In Song
Venue: CIKM
Year: 2025

There is currently a controversy as to whether LLM-based relevance assessment can replace manual relevance assessment for evaluating search engines accurately at least at the run level (e.g., ranking TREC runs by mean nDCG) if not at the individual topic level (e.g., computing an nDCG score for a Search Engine Result Page). This study utilises an NTCIR web search test collection that features highly reliable human relevance labels (reflecting the collective view of eight independent assessors per topic) to complement prior findings from the skeptic camp. Our experiments show that LLM-based assessment (using Llama and Qwen) cannot replace human assessment even for ranking systems in terms of mean nDCG. More importantly, LLM-based assessment lacks discriminative power: it misses many statistically significant differences that manual assessment can detect. Furthermore, LLM-based assessment occasionally yields potential false alarms in terms of statistical significance, which may let researchers reach incorrect conclusions.