chore: update confs

Author: actions-user

arxiv.json

@@ -43580,5 +43580,40 @@
         "pub_date": "2025-03-26",
         "summary": "Thematic analysis (TA) is a widely used qualitative approach for uncovering latent meanings in unstructured text data. TA provides valuable insights in healthcare but is resource-intensive. Large Language Models (LLMs) have been introduced to perform TA, yet their applications in healthcare remain unexplored. Here, we propose TAMA: A Human-AI Collaborative Thematic Analysis framework using Multi-Agent LLMs for clinical interviews. We leverage the scalability and coherence of multi-agent systems through structured conversations between agents and coordinate the expertise of cardiac experts in TA. Using interview transcripts from parents of children with Anomalous Aortic Origin of a Coronary Artery (AAOCA), a rare congenital heart disease, we demonstrate that TAMA outperforms existing LLM-assisted TA approaches, achieving higher thematic hit rate, coverage, and distinctiveness. TAMA demonstrates strong potential for automated TA in clinical settings by leveraging multi-agent LLM systems with human-in-the-loop integration by enhancing quality while significantly reducing manual workload.",
         "translated": "主题分析（TA）是一种广泛应用于非结构化文本数据潜在意义挖掘的定性研究方法。该方法在医疗健康领域能提供重要洞见，但存在资源消耗大的问题。尽管大型语言模型（LLM）已被引入用于执行TA任务，但其在医疗健康领域的应用仍有待探索。本研究提出TAMA框架——基于多智能体LLM的人机协同主题分析系统，专门针对临床访谈场景。我们通过智能体间的结构化对话发挥多智能体系统的可扩展性与一致性优势，并整合心脏专科医生在TA过程中的专业知识。以罕见先天性心脏病\"冠状动脉主动脉起源异常（AAOCA）\"患儿父母的访谈转录文本为实验材料，研究证明TAMA在主题命中率、覆盖度和区分度等指标上均优于现有LLM辅助TA方法。该框架通过\"人在回路\"的多智能体LLM系统，在显著降低人工工作量的同时提升分析质量，展现了在临床场景中实现自动化主题分析的强大潜力。\n\n（注：根据学术翻译规范，对以下术语进行统一处理：\n1. \"latent meanings\"译为\"潜在意义\"而非\"隐藏含义\"，更符合社会科学研究语境\n2. \"hit rate\"采用\"命中率\"这一计量学术语标准译法\n3. \"human-in-the-loop\"译为专业术语\"人在回路\"，保留人机协同的技术内涵\n4. 疾病名称\"AAOCA\"首次出现时保留英文缩写并标注全称中文译名，符合医学论文翻译标准）"
+    },
+    {
+        "title": "CombiGCN: An effective GCN model for Recommender System",
+        "url": "http://arxiv.org/abs/2503.21471v1",
+        "pub_date": "2025-03-27",
+        "summary": "Graph Neural Networks (GNNs) have opened up a potential line of research for collaborative filtering (CF). The key power of GNNs is based on injecting collaborative signal into user and item embeddings which will contain information about user-item interactions after that. However, there are still some unsatisfactory points for a CF model that GNNs could have done better. The way in which the collaborative signal are extracted through an implicit feedback matrix that is essentially built on top of the message-passing architecture of GNNs, and it only helps to update the embedding based on the value of the items (or users) embeddings neighboring. By identifying the similarity weight of users through their interaction history, a key concept of CF, we endeavor to build a user-user weighted connection graph based on their similarity weight.   In this study, we propose a recommendation framework, CombiGCN, in which item embeddings are only linearly propagated on the user-item interaction graph, while user embeddings are propagated simultaneously on both the user-user weighted connection graph and user-item interaction graph graphs with Light Graph Convolution (LGC) and combined in a simpler method by using the weighted sum of the embeddings for each layer. We also conducted experiments comparing CombiGCN with several state-of-the-art models on three real-world datasets.",
+        "translated": "图神经网络（GNNs）为协同过滤（CF）研究开辟了新的可能性。GNN的核心优势在于能将协同信号注入用户和项目嵌入表示中，使嵌入包含用户-项目交互信息。然而现有基于GNN的CF模型仍存在改进空间：当前通过隐式反馈矩阵提取协同信号的方式，本质上依赖于GNN的消息传递架构，仅能根据相邻项目（或用户）嵌入值更新当前嵌入表示。本研究基于CF的核心思想——通过用户交互历史计算相似度权重，构建了用户-用户加权连接图。\n\n我们提出CombiGCN推荐框架，其创新点在于：1）项目嵌入仅在线性传播的用户-项目交互图上更新；2）用户嵌入同时在用户-用户加权连接图和用户-项目交互图上通过轻量图卷积（LGC）进行传播；3）采用更简单的加权求和方式逐层融合双图传播的嵌入表示。通过在三个真实数据集上与多个前沿模型的对比实验，验证了该框架的有效性。\n\n（翻译说明：\n1. 专业术语处理：\"message-passing architecture\"译为\"消息传递架构\"，\"Light Graph Convolution\"保留英文缩写并补充\"轻量图卷积\"全称\n2. 复杂句式拆分：将原文两个长句拆分为三个短句，符合中文表达习惯\n3. 被动语态转化：\"are extracted\"转换为主动态\"提取\"\n4. 概念显化处理：\"key concept of CF\"补充说明为\"CF的核心思想\"\n5. 列表式呈现技术要点：使用数字序号突出框架创新点\n6. 学术规范：\"CombiGCN\"首次出现时补充说明为\"推荐框架\"）"
+    },
+    {
+        "title": "Improvement Graph Convolution Collaborative Filtering with Weighted\n  addition input",
+        "url": "http://arxiv.org/abs/2503.21468v1",
+        "pub_date": "2025-03-27",
+        "summary": "Graph Neural Networks have been extensively applied in the field of machine learning to find features of graphs, and recommendation systems are no exception. The ratings of users on considered items can be represented by graphs which are input for many efficient models to find out the characteristics of the users and the items. From these insights, relevant items are recommended to users. However, user's decisions on the items have varying degrees of effects on different users, and this information should be learned so as not to be lost in the process of information mining.   In this publication, we propose to build an additional graph showing the recommended weight of an item to a target user to improve the accuracy of GNN models. Although the users' friendships were not recorded, their correlation was still evident through the commonalities in consumption behavior. We build a model WiGCN (Weighted input GCN) to describe and experiment on well-known datasets. Conclusions will be stated after comparing our results with state-of-the-art such as GCMC, NGCF and LightGCN. The source code is also included at https://github.com/trantin84/WiGCN.",
+        "translated": "图神经网络已被广泛应用于机器学习领域以挖掘图结构特征，在推荐系统中也不例外。用户对目标项目的评分数据可以表示为图结构，作为众多高效模型的输入来提取用户和项目的特性。基于这些特征洞察，系统能够向用户推荐相关项目。然而，用户对项目的决策行为对不同用户会产生不同程度的影响，这些信息应在特征挖掘过程中被有效学习以避免信息丢失。  \n\n在本研究中，我们提出构建一个附加图结构来表征项目对目标用户的推荐权重，以此提升图神经网络模型的预测精度。尽管原始数据未记录用户间的社交关系，但通过消费行为的共性仍能清晰呈现其内在关联性。我们构建了加权输入图卷积网络（WiGCN）模型，并在多个经典数据集上进行实验验证。通过将实验结果与GCMC、NGCF和LightGCN等前沿模型进行对比分析，我们将给出最终结论。相关源代码已开源在https://github.com/trantin84/WiGCN。  \n\n（注：根据学术论文翻译规范，对以下专业术语进行了标准化处理：  \n1. \"characteristics\"译为\"特性\"而非\"特征\"，与后文\"特征挖掘\"形成术语区分  \n2. \"state-of-the-art\"采用\"前沿模型\"的意译处理  \n3. 技术名词\"GNN/GCN\"首次出现时使用全称\"图神经网络/图卷积网络\"，后文使用缩写  \n4. 被动语态转换为主动句式（如\"can be represented\"译为\"可以表示\"→\"表示为\"）  \n5. 长难句拆分重组（如最后一句分译为两个中文句子）以符合中文表达习惯）"
+    },
+    {
+        "title": "Bias-Aware Agent: Enhancing Fairness in AI-Driven Knowledge Retrieval",
+        "url": "http://arxiv.org/abs/2503.21237v1",
+        "pub_date": "2025-03-27",
+        "summary": "Advancements in retrieving accessible information have evolved faster in the last few years compared to the decades since the internet's creation. Search engines, like Google, have been the number one way to find relevant data. They have always relied on the user's abilities to find the best information in its billions of links and sources at everybody's fingertips. The advent of large language models (LLMs) has completely transformed the field of information retrieval. The LLMs excel not only at retrieving relevant knowledge but also at summarizing it effectively, making information more accessible and consumable for users. On top of it, the rise of AI Agents has introduced another aspect to information retrieval i.e. dynamic information retrieval which enables the integration of real-time data such as weather forecasts, and financial data with the knowledge base to curate context-aware knowledge. However, despite these advancements the agents remain susceptible to issues of bias and fairness, challenges deeply rooted within the knowledge base and training of LLMs. This study introduces a novel approach to bias-aware knowledge retrieval by leveraging agentic framework and the innovative use of bias detectors as tools to identify and highlight inherent biases in the retrieved content. By empowering users with transparency and awareness, this approach aims to foster more equitable information systems and promote the development of responsible AI.",
+        "translated": "与互联网诞生后的数十年相比，近年在可访问信息检索领域取得的进展更为迅猛。以谷歌为代表的搜索引擎始终是获取相关数据的首要途径，但其运作一直依赖于用户从海量链接和触手可及的信息源中筛选最优内容的能力。大型语言模型（LLMs）的出现彻底革新了信息检索领域——这些模型不仅擅长检索相关知识，更能高效进行信息摘要，大幅提升信息的可获取性与可理解性。而AI智能体的兴起则为信息检索增添了动态维度：通过将天气预报、金融数据等实时信息与知识库整合，实现情境感知的知识构建。然而尽管取得这些突破，当前系统仍难以规避偏见与公平性问题，这些挑战根植于知识库构建与LLM训练过程之中。本研究提出了一种基于智能体框架的创新方法，通过将偏见检测器作为工具来识别并凸显检索内容中的固有偏差，实现偏差感知的知识检索。该方案通过增强信息透明度和用户认知，致力于构建更公平的信息系统，推动负责任人工智能的发展。\n\n（翻译说明：\n1. 专业术语处理：LLMs、AI Agents等缩写首次出现时保留英文并标注中文全称，后续直接使用缩写\n2. 技术概念转换：\"dynamic information retrieval\"译为\"动态信息检索\"，\"context-aware knowledge\"译为\"情境感知知识\"符合计算机领域术语规范\n3. 长句拆分：将原文复合长句按中文表达习惯分解为多个短句，如通过破折号和冒号保持逻辑衔接\n4. 被动语态转换：\"have been relied on\"转化为主动式\"其运作依赖于\"更符合中文表达\n5. 学术风格保持：使用\"构建\"、\"规避\"等正式词汇，避免口语化表达\n6. 创新点突出：通过\"破折号+转折词\"的结构强调技术突破，使用\"方案\"、\"致力于\"等体现研究价值的表述）"
+    },
+    {
+        "title": "Are We Solving a Well-Defined Problem? A Task-Centric Perspective on\n  Recommendation Tasks",
+        "url": "http://arxiv.org/abs/2503.21188v1",
+        "pub_date": "2025-03-27",
+        "summary": "Recommender systems (RecSys) leverage user interaction history to predict and suggest relevant items, shaping user experiences across various domains. While many studies adopt a general problem definition, i.e., to recommend preferred items to users based on past interactions, such abstraction often lacks the domain-specific nuances necessary for practical deployment. However, models are frequently evaluated using datasets from online recommender platforms, which inherently reflect these specificities. In this paper, we analyze RecSys task formulations, emphasizing key components such as input-output structures, temporal dynamics, and candidate item selection. All these factors directly impact offline evaluation. We further examine the complexities of user-item interactions, including decision-making costs, multi-step engagements, and unobservable interactions, which may influence model design and loss functions. Additionally, we explore the balance between task specificity and model generalizability, highlighting how well-defined task formulations serve as the foundation for robust evaluation and effective solution development. By clarifying task definitions and their implications, this work provides a structured perspective on RecSys research. The goal is to help researchers better navigate the field, particularly in understanding specificities of the RecSys tasks and ensuring fair and meaningful evaluations.",
+        "translated": "推荐系统（RecSys）通过利用用户交互历史来预测和推荐相关项目，从而塑造各领域的用户体验。尽管许多研究采用通用的问题定义（即根据历史交互为用户推荐偏好项目），此类抽象表述往往缺乏实际部署所需的领域特定细节。然而，模型评估却常使用源自在线推荐平台的数据集，这些数据本质上反映了这些特定性。本文系统分析了推荐系统的任务构建框架，重点阐释了输入输出结构、时序动态特征以及候选项目选择等核心要素——这些因素均会直接影响离线评估效果。我们进一步剖析了用户-项目交互中的复杂特性，包括决策成本、多步骤参与行为以及不可观测的交互，这些因素可能影响模型设计与损失函数构建。此外，我们探讨了任务特定性与模型泛化能力之间的平衡关系，强调清晰的任务构建作为稳健评估和有效方案开发的基础作用。通过厘清任务定义及其深层影响，本研究为推荐系统研究提供了结构化视角，旨在帮助研究者更精准地把握领域方向，特别是理解推荐系统任务的特殊性，并确保评估过程的公正性与意义性。"
+    },
+    {
+        "title": "Network Density Analysis of Health Seeking Behavior in Metro Manila: A\n  Retrospective Analysis on COVID-19 Google Trends Data",
+        "url": "http://arxiv.org/abs/2503.21162v1",
+        "pub_date": "2025-03-27",
+        "summary": "This study examined the temporal aspect of COVID-19-related health-seeking behavior in Metro Manila, National Capital Region, Philippines through a network density analysis of Google Trends data. A total of 15 keywords across five categories (English symptoms, Filipino symptoms, face wearing, quarantine, and new normal) were examined using both 15-day and 30-day rolling windows from March 2020 to March 2021. The methodology involved constructing network graphs using distance correlation coefficients at varying thresholds (0.4, 0.5, 0.6, and 0.8) and analyzing the time-series data of network density and clustering coefficients. Results revealed three key findings: (1) an inverse relationship between the threshold values and network metrics, indicating that higher thresholds provide more meaningful keyword relationships; (2) exceptionally high network connectivity during the initial pandemic months followed by gradual decline; and (3) distinct patterns in keyword relationships, transitioning from policy-focused searches to more symptom-specific queries as the pandemic temporally progressed. The 30-day window analysis showed more stable, but less search activities compared to the 15-day windows, suggesting stronger correlations in immediate search behaviors. These insights are helpful for health communication because it emphasizes the need of a strategic and conscientious information dissemination from the government or the private sector based on the networked search behavior (e.g. prioritizing to inform select symptoms rather than an overview of what the coronavirus is).",
+        "translated": "本研究通过谷歌趋势数据的网络密度分析，考察了菲律宾国家首都区大马尼拉地区COVID-19相关健康寻求行为的时间特征。采用15天和30天滚动窗口（2020年3月至2021年3月），对五大类（英文症状、菲律宾语症状、面部防护、隔离、新常态）共计15个关键词进行分析。方法学包括：基于不同阈值（0.4、0.5、0.6和0.8）的距离相关系数构建网络图，并对网络密度与聚类系数的时间序列数据进行解析。研究结果揭示三个关键发现：（1）阈值与网络指标呈负相关，表明较高阈值能呈现更具意义的关键词关联；（2）疫情初期网络连接性异常高涨，随后逐步衰减；（3）关键词关联呈现明显模式转变——随时间推移，公众搜索焦点从政策导向逐步转向症状特异性查询。30天窗口分析显示其相关性虽更稳定但搜索活跃度低于15天窗口，暗示即时搜索行为具有更强关联性。这些发现对健康传播具有重要启示：基于网络化搜索行为特征（例如优先通报特定症状而非冠状病毒概述），政府或私营部门需实施更具战略性与针对性的信息传播策略。"
     }
 ]