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Decoding Activist Public Opinion in Decentralized Self-Organized Protests Using LLM 利用 LLM 解码分散自发抗议活动中的积极分子舆论
IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-06-05 DOI: 10.1109/TCSS.2024.3398815
Baoyu Zhang;Tao Chen;Xiao Wang;Qiang Li;Weishan Zhang;Fei-Yue Wang
Based on an investigation of online public opinion on the Nahel Merzouk protests in France, an approach for analyzing and predicting public opinion on protests based on large language model (LLM) is proposed, revealing the impact of emerging social media on the protests. We demonstrate that protests generate public opinion on social media with some lag, but that comment sentiment and expression are consistent with protest trends. As the protests unfolded, we analyzed the evolution of public sentiment. We constructed the prompt based on historical data to predict the protests using the p-tuning and Lora approach to fine-tune LLM. In addition, we discuss how to use blockchain technology to optimize distributed, self-organizing protests and reduce the potential for disinformation and violent conflict.
基于对法国 Nahel Merzouk 抗议活动的网络舆论调查,我们提出了一种基于大语言模型(LLM)分析和预测抗议活动舆论的方法,揭示了新兴社交媒体对抗议活动的影响。我们证明,抗议活动在社交媒体上引发的舆论具有一定的滞后性,但评论情绪和表达与抗议活动的趋势是一致的。随着抗议活动的展开,我们分析了公众情绪的演变。我们在历史数据的基础上构建了预测抗议活动的提示,并使用 p-tuning 和 Lora 方法对 LLM 进行了微调。此外,我们还讨论了如何利用区块链技术优化分布式自组织抗议活动,并降低虚假信息和暴力冲突的可能性。
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引用次数: 0
Incentive Mechanism for Redactable Blockchain Governance: An Evolutionary Game Approach 可重构区块链治理的激励机制:进化博弈方法
IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-06-05 DOI: 10.1109/TCSS.2024.3398044
Jiaxiang Sun;Rong Zhao;Haoran Yin;Wei Cai
Blockchain technology has garnered significant attention in recent years due to its capacity to offer secure and transparent transactional systems. However, the technology's inherent immutability can present challenges in specific scenarios. While earlier research has concentrated on the development of redactable blockchain, existing solutions have primarily focused on the modification mechanism, often overlooking the critical element of an incentive mechanism for governance, which is paramount for ensuring the security of redactable blockchain. Some previous researches have explored the design of incentive mechanisms, but these studies exhibit certain shortcomings. To promote active participation, we have designed an incentive mechanism rooted in evolutionary game theory for stakeholders in redactable blockchain, aiming to facilitate the governance of redactable blockchain. Furthermore, we have conducted a comprehensive simulation founded on game-theoretic analysis. The results substantiate the effectiveness of our redactable blockchain incentive mechanism in achieving its intended objectives.
近年来,区块链技术因其能够提供安全透明的交易系统而备受关注。然而,该技术固有的不可更改性可能会在特定场景中带来挑战。虽然早期的研究主要集中在可编辑区块链的开发上,但现有的解决方案主要集中在修改机制上,往往忽略了治理激励机制这一关键要素,而这对于确保可编辑区块链的安全性至关重要。之前的一些研究对激励机制的设计进行了探索,但这些研究表现出一定的缺陷。为了促进可编辑区块链利益相关者的积极参与,我们设计了一种基于进化博弈论的激励机制,旨在促进可编辑区块链的治理。此外,我们还在博弈论分析的基础上进行了综合模拟。结果证明了我们的可编辑区块链激励机制在实现预期目标方面的有效性。
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引用次数: 0
Contextual Semantics Interaction Graph Embedding Learning for Recommender Systems 面向推荐系统的上下文语义交互图嵌入式学习
IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-06-05 DOI: 10.1109/TCSS.2024.3394701
Shiyu Zhao;Yong Zhang;Mengran Li;Xinglin Piao;Baocai Yin
Recommender systems have become an indispensable tool in today's digital age, significantly enhancing user engagement on various online platforms by curating personalized item recommendations tailored to individual preferences. While the field has long been dominated by the collaborative filtering technique, which primarily leverages user–item interaction data, it often falls short in encapsulating the rich contextual intricacies and evolving dynamics inherent to these interactions. Recognizing this limitation, our research introduces the contextual semantic interaction graph embedding (CSI-GE) method. This advanced model incorporates a dynamic hop window within a multilayer graph convolutional network, ensuring a comprehensive extraction of both immediate and evolving contextual features. By amalgamating self-supervised contrastive learning, we achieve a refinement of user and item embeddings. Furthermore, our innovative variance–invariance–covariance (VIC) regularization-based loss function fortifies the robustness of these embeddings. Through rigorous testing, CSI-GE consistently outperformed contemporary methods, underscoring its superior accuracy and stability.
推荐系统已成为当今数字时代不可或缺的工具,通过根据个人喜好策划个性化的项目推荐,大大提高了用户在各种在线平台上的参与度。长期以来,协同过滤技术一直占据着该领域的主导地位,它主要利用用户与物品之间的交互数据,但往往无法囊括这些交互所固有的丰富而错综复杂的上下文和不断变化的动态。认识到这一局限性后,我们的研究引入了上下文语义交互图嵌入(CSI-GE)方法。这种先进的模型在多层图卷积网络中加入了动态跳转窗口,确保全面提取即时和不断变化的上下文特征。通过融合自监督对比学习,我们实现了用户和项目嵌入的细化。此外,我们创新的基于方差-方差-协方差(VIC)正则化的损失函数加强了这些嵌入的鲁棒性。通过严格的测试,CSI-GE 的性能始终优于同类方法,突出了其卓越的准确性和稳定性。
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引用次数: 0
CGNN: A Compatibility-Aware Graph Neural Network for Social Media Bot Detection CGNN:用于社交媒体机器人检测的兼容性感知图神经网络
IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-06-05 DOI: 10.1109/TCSS.2024.3396413
Haitao Huang;Hu Tian;Xiaolong Zheng;Xingwei Zhang;Daniel Dajun Zeng;Fei-Yue Wang
With the rise and prevalence of social bots, their negative impacts on society are gradually recognized, prompting research attention to effective detection and countermeasures. Recently, graph neural networks (GNNs) have flourished and have been applied to social bot detection research, improving the performance of detection methods effectively. However, existing GNN-based social bot detection methods often fail to account for the heterogeneous associations among users within social media contexts, especially the heterogeneous integration of social bots into human communities within the network. To address this challenge, we propose a heterogeneous compatibility perspective for social bot detection, in which we preserve more detailed information about the varying associations between neighbors in social media contexts. Subsequently, we develop a compatibility-aware graph neural network (CGNN) for social bot detection. CGNN consists of an efficient feature processing module, and a lightweight compatibility-aware GNN encoder, which enhances the model’s capacity to depict heterogeneous neighbor relations by emulating the heterogeneous compatibility function. Through extensive experiments, we showed that our CGNN outperforms the existing state-of-the-art (SOTA) method on three commonly used social bot detection benchmarks while utilizing only about 2% of the parameter size and 10% of the training time compared with the SOTA method. Finally, further experimental analysis indicates that CGNN can identify different edge categories to a significant extent. These findings, along with the ablation study, provide strong evidence supporting the enhancement of GNN’s capacity to depict heterogeneous neighbor associations on social media bot detection tasks.
随着社交机器人的兴起和盛行,其对社会的负面影响逐渐被人们所认识,促使人们开始关注有效的检测和对策研究。近年来,图神经网络(GNN)得到了蓬勃发展,并被应用于社交僵尸的检测研究,有效提高了检测方法的性能。然而,现有的基于图神经网络的社交僵尸检测方法往往不能考虑社交媒体语境中用户之间的异构关联,尤其是社交僵尸与网络中人类社区的异构整合。为了应对这一挑战,我们提出了社交僵尸检测的异构兼容性视角,其中我们保留了社交媒体上下文中邻居之间不同关联的更详细信息。随后,我们开发了一种用于社交僵尸检测的兼容性感知图神经网络(CGNN)。图神经网络由一个高效的特征处理模块和一个轻量级兼容性感知图神经网络编码器组成,后者通过模拟异构兼容性函数增强了模型描绘异构邻居关系的能力。通过大量实验,我们发现在三个常用的社交僵尸检测基准上,我们的 CGNN 优于现有的最先进方法(SOTA),而与 SOTA 方法相比,CGNN 的参数大小仅为 SOTA 方法的 2%,训练时间仅为 SOTA 方法的 10%。最后,进一步的实验分析表明,CGNN 可以在很大程度上识别不同的边缘类别。这些发现以及消融研究为增强 GNN 在社交媒体僵尸检测任务中描绘异质邻居关联的能力提供了有力证据。
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引用次数: 0
XLoCoFC: A Fast Fuzzy Community Detection Approach Based on Expandable Local Communities Through Max-Membership Degree Propagation XLoCoFC:基于通过最大成员度传播的可扩展本地社群的快速模糊社群检测方法
IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-06-05 DOI: 10.1109/TCSS.2024.3392069
Uttam K. Roy;Pranab K. Muhuri;Sajib K. Biswas
Fuzzy community detection (FCD) aims to reveal the community structure by allocating quantitative values to nodes across different communities. This article proposes a fast FCD approach called the Expandable Local Community based Fuzzy Community (XLoCoFC) detection method based on max-membership degree propagation (max-MDP) and normalized peripheral similarity index ($ boldsymbol{n}mathbf{P}mathbf{S}mathbf{I}$). Initially, nodes having comparatively higher $ boldsymbol{n}mathbf{P}mathbf{S}mathbf{I}$ values are considered as topologically dominating nodes and selected as seeds. For an initial community, called local community, seed’s $ boldsymbol{n}mathbf{P}mathbf{S}mathbf{I}$ values from the respective neighbors’ peripheries are utilized as the neighbors’ membership degrees. Then an iterative process propagates max-membership degrees from nodes to nodes, and $ boldsymbol{n}mathbf{P}mathbf{S}mathbf{I}$ values are used as factors in the propagation. In this propagation, local communities having more dominating nodes expand and others contract. The propagation process converges very quickly. Such simplicity in its design makes our proposed XLoCoFC approach to be very fast in finding community structures on large networks. Time complexity of the proposed approach is $ boldsymbol{O}left(boldsymbol{n}boldsymbol{d}^{2}times mathbf{lo}mathbf{g}_{2} boldsymbol{d}+mathbf{k}mathbf{l}mathbf{q}right)$ which is significantly less than the majority of the FCD algorithms, for whom it is either $ boldsymbol{O}left(boldsymbol{n}^{2}right)$ or more. Moreover, XLoCoFC has no dependence on any network feature. It does not require tuning of any parameter which may impact its output. To demonstrate the working of the proposed XLoCoFC approach, we conduct extensive performance analysis comparatively by executing a set of existing approaches on several popular real-life and synthetic networks with number of nodes ranging from 24 to 1134 890. Evaluation of the results considering the accuracy and quality metrics as well as a group MCDM technique clearly establishes the superiority of our approach over others.
模糊社区检测(FCD)旨在通过为不同社区的节点分配定量值来揭示社区结构。本文提出了一种基于最大成员度传播(max-MDP)和归一化外围相似性指数($ boldsymbol{n}mathbf{P}mathbf{S}mathbf{I}$)的快速 FCD 方法,即基于模糊社区的可扩展本地社区(XLoCoFC)检测方法。最初,具有相对较高 $boldsymbol{n}mathbf{P}mathbf{S}mathbf{I}$ 值的节点被视为拓扑主导节点,并被选为种子节点。对于一个被称为本地社区的初始社区,种子的 $ boldsymbol{n}mathbf{P}mathbf{S}mathbf{I}$ 值从各自邻居的外围被用作邻居的成员度。然后,一个迭代过程将最大成员度从节点传播到节点,$ boldsymbol{n}mathbf{P}mathbf{S}mathbf{I}$值被用作传播中的因子。在这个传播过程中,拥有更多支配节点的局部群落会扩大,而其他群落则会缩小。传播过程收敛得非常快。这种简单的设计使我们提出的 XLoCoFC 方法在大型网络中寻找群落结构时非常快速。所提方法的时间复杂度为 $boldsymbol{O}left(boldsymbol{n}boldsymbol{d}^{2}timesmathbf{lo}mathbf{g}_{2}+(mathbf{k}mathbf{l}mathbf{q}right)$,这比大多数 FCD 算法都要少得多,对它们来说,要么是 $ boldsymbol{O}left(boldsymbol{n}^{2}right)$,要么更多。此外,XLoCoFC 不依赖于任何网络特征。它不需要调整任何可能影响其输出的参数。为了证明所提出的 XLoCoFC 方法的工作原理,我们在几个流行的真实网络和合成网络上执行了一系列现有方法,节点数从 24 到 1134 890 不等,从而进行了广泛的性能比较分析。根据准确性和质量指标以及分组 MCDM 技术对结果进行的评估清楚地证明了我们的方法优于其他方法。
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引用次数: 0
Multisource-Knowledge-Based Approach for Crowd Evacuation Navigation 基于多源知识的人群疏散导航方法
IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-04-30 DOI: 10.1109/TCSS.2024.3381840
Pengfei Zhang;Kun Zhao;Hong Liu;Wenhao Li
In crowd evacuation research, the knowledge contained in crowd evacuation is very complex and is multisource. Crowd evacuation scenarios restrict pedestrians’ movement decision-making, and the movement states of the crowd imply the movement characteristics. However, the existing studies on crowd evacuation navigation approach cannot make full use of the complex and multisource crowd evacuation knowledge, which reduces the effect of the evacuation navigation. To solve this problem, a new crowd evacuation navigation approach based on multisource knowledge is proposed. First, we collect relevant data on crowd evacuation using an image sensor network and establish a crowd evacuation knowledge graph to organize and store this data. Second, the explicit knowledge of scene structure and crowd movements is represented based on the crowd evacuation knowledge graph. Then, a deep-learning-based tacit knowledge model (DLTKM) is designed to extract the tacit knowledge of different groups and scene entities. Finally, a new crowd evacuation navigation approach based on wireless sensor network and related knowledge representations is designed to plan evacuation paths for evacuees. The experiment results show that this approach can plan reasonable evacuation paths for pedestrians, and improve the efficiency of crowd evacuations.
在人群疏散研究中,人群疏散所包含的知识非常复杂,而且是多源的。人群疏散场景限制了行人的移动决策,而人群的移动状态意味着移动特征。然而,现有的人群疏散导航方法研究无法充分利用复杂且多源的人群疏散知识,从而降低了疏散导航的效果。为解决这一问题,本文提出了一种基于多源知识的新型人群疏散导航方法。首先,我们利用图像传感器网络收集人群疏散的相关数据,并建立人群疏散知识图谱来组织和存储这些数据。其次,基于人群疏散知识图谱来表示场景结构和人群移动的显性知识。然后,设计基于深度学习的隐性知识模型(DLTKM),提取不同群体和场景实体的隐性知识。最后,设计了一种基于无线传感器网络和相关知识表征的新型人群疏散导航方法,为疏散人员规划疏散路径。实验结果表明,该方法可以为行人规划合理的疏散路径,提高人群疏散的效率。
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引用次数: 0
AI-Enabled Deep Depression Detection and Evaluation Informed by DSM-5-TR 基于 DSM-5-TR 的人工智能深度抑郁检测与评估
IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-04-30 DOI: 10.1109/TCSS.2024.3382139
Tabia Tanzin Prama;Md. Saiful Islam;Md Musfique Anwar;Ifrat Jahan
Depression, a prevalent and debilitating mental health disorder, affects millions of individuals worldwide, profoundly impacting their quality of life. Early identification and diagnosis of depression and its intensity are crucial for effective treatment and management. However, many people with depression do not seek professional help, especially in the early stages. In recent years, social media platforms like Twitter have gained popularity as spaces for sharing personal thoughts and emotions, including sensitive signals indicative of serious issues such as self-harm, suicidal thoughts, or illegal activities. These signals may help us to identify depression-related tweets and determine whether an individual is suffering from depression. This research focuses on utilizing artificial intelligence and deep learning (DL) models to categorize tweets related to depression and measure its intensity. The proposed approach combines emotional features, topical events, and behavioral-biometric signals to train the long short-term memory (LSTM)-based DL models. To create a comprehensive dataset, we collaborated with an expert psychologist who followed the clinical assessment procedure outlined in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5-TR) to label 95 322 tweets as “non-depressed” or “depressed,” further categorized into “mild,” “moderate,” and “severe” intensity levels. Through a series of experiments, our proposed method achieved superior performance compared to baseline models, yielding a mean squared error of 0.002336 and the highest $R$2 value of 0.61. These results highlight the accuracy and potential applications of our approach in automatic depression screening and monitoring on social media platforms.
抑郁症是一种普遍存在且使人衰弱的精神疾病,影响着全球数百万人,严重影响着他们的生活质量。早期识别和诊断抑郁症及其强度对于有效治疗和管理至关重要。然而,许多抑郁症患者并不寻求专业帮助,尤其是在早期阶段。近年来,Twitter 等社交媒体平台越来越受欢迎,成为分享个人想法和情绪的空间,其中包括表明存在严重问题(如自残、自杀想法或非法活动)的敏感信号。这些信号可以帮助我们识别与抑郁症有关的推文,并确定个人是否患有抑郁症。本研究的重点是利用人工智能和深度学习(DL)模型对与抑郁相关的推文进行分类,并测量其强度。所提出的方法结合了情感特征、热点事件和行为生物特征信号来训练基于长短期记忆(LSTM)的深度学习模型。为了创建一个全面的数据集,我们与一位心理专家合作,由他按照《精神疾病诊断与统计手册第五版》(DSM-5-TR)中规定的临床评估程序,将 95 322 条推文标记为 "非抑郁 "或 "抑郁",并进一步分为 "轻度"、"中度 "和 "重度 "强度级别。通过一系列实验,与基线模型相比,我们提出的方法取得了优异的性能,平均平方误差为 0.002336,最高 $R$2 值为 0.61。这些结果凸显了我们的方法在社交媒体平台上自动筛选和监测抑郁症方面的准确性和潜在应用。
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引用次数: 0
Evolutionary Dynamics of Preguidance Strategies in Population Games 群体博弈中预引导策略的进化动力学
IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-04-30 DOI: 10.1109/TCSS.2024.3386501
Linjie Liu;Xiaojie Chen
Promoting cooperation among conflicting entities in human society and intelligent systems is a formidable task. One potential solution could involve the formulation of incentives designed to decrease the benefits of noncooperators and/or increase the rewards for cooperators. We put forth a novel incentive approach, specifically, a guidance strategy where certain cooperators willingly bear a cost to alter the actions of agents who intend to defect prior to the actual commencement of a game. We introduce an innovative game-theoretical framework that sheds light on the dynamics of guidance strategies, encompassing both peer guidance and pool guidance. Under the peer guidance scheme, each guider independently incurs the cost to influence agents intending to defect, whereas in the pool guidance scheme, guiders organically establish an institution to influence agents prone to free riding. Regardless of whether a peer or pool guidance scheme is utilized, the implementation of a guidance strategy has proven to be remarkably effective in reducing the instances of pure cooperation, also known as second-order free riding. Intriguingly, our result suggests that the pool guidance strategy demonstrates a more potent deterrent effect on second-order free-riding behavior than the peer guidance strategy, particularly when the cost of guidance is exceptionally high. These findings underscore the significance of preguidance in fostering cooperation in human and multiagent AI systems and could offer valuable insights for the development of a regulatory mechanism for preemptive guidance and subsequent punishment.
促进人类社会和智能系统中相互冲突的实体之间的合作是一项艰巨的任务。一种可能的解决方案是制定激励措施,以减少非合作者的利益和/或增加合作者的回报。我们提出了一种新颖的激励方法,特别是一种引导策略,即在博弈实际开始之前,某些合作者自愿承担一定的成本,以改变打算叛变的代理的行动。我们引入了一个创新的博弈论框架,它揭示了指导策略的动态变化,包括同伴指导和集合指导。在同伴指导方案中,每个指导者都要独立承担影响有意叛逃的代理人的成本,而在集合指导方案中,指导者会有机地建立一个机构来影响容易搭便车的代理人。事实证明,无论采用同伴指导方案还是集合指导方案,实施指导策略都能显著减少纯合作(也称二阶搭便车)的情况。耐人寻味的是,我们的结果表明,与同伴指导策略相比,集合指导策略对二阶搭便车行为的威慑力更大,尤其是在指导成本特别高的情况下。这些发现强调了预先指导在促进人类和多机器人人工智能系统合作中的重要作用,并为开发预先指导和后续惩罚的监管机制提供了宝贵的见解。
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引用次数: 0
Adaptive Dual-Space Network With Multigraph Fusion for EEG-Based Emotion Recognition 基于脑电图情感识别的多图融合自适应双空间网络
IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-04-30 DOI: 10.1109/TCSS.2024.3386621
Mengqing Ye;C. L. Philip Chen;Wenming Zheng;Tong Zhang
Most of the work on electroencephalogram (EEG)-based emotion recognition aims to extract the distinguishing features from high-dimensional EEG signals, ignoring the complementarity of information between EEG latent space and graph space. Furthermore, the influence of brain connectivity on emotions encompasses both physical structure and functional connectivity, which may have varying degrees of importance for different individuals. To address these issues, this article introduces an adaptive dual-space network (ADS-Net) with multigraph fusion aimed at capturing more comprehensive information by integrating dual-space representations. Specifically, ADS-Net models the spatial correlation of EEG channels in graph topological space, while exploring long-range dependencies and frequency relationships from EEG data in latent space. Subsequently, these representations are adaptively combined through an innovative gated fusion approach to extract complementary corepresentations. Moreover, drawing on the principles of brain connectivity theory, the proposed method constructs a multigraph to indicate the associativity of EEG channels. To further capture individual differences, an adaptive multigraph fusion mechanism is developed for the dynamic integration of physical and functional connectivity graphs. When compared to state-of-the-art methods, the superior experimental results underscore the effectiveness and broad applicability of the proposed method.
大多数基于脑电图(EEG)的情绪识别工作都旨在从高维脑电信号中提取识别特征,而忽略了脑电图潜空间和图空间之间的信息互补性。此外,大脑连通性对情绪的影响包括物理结构和功能连通性两方面,而这两方面对不同个体的重要性可能各不相同。为了解决这些问题,本文介绍了一种具有多图融合功能的自适应双空间网络(ADS-Net),旨在通过整合双空间表征来捕捉更全面的信息。具体来说,ADS-Net 在图拓扑空间中对脑电图通道的空间相关性进行建模,同时在潜空间中探索脑电图数据的长程依赖性和频率关系。随后,这些表征通过创新的门控融合方法进行自适应组合,以提取互补的核心表征。此外,借鉴大脑连接理论的原理,所提出的方法构建了一个多图,以显示脑电图通道的关联性。为了进一步捕捉个体差异,还开发了一种自适应多图融合机制,用于动态整合物理和功能连接图。与最先进的方法相比,卓越的实验结果凸显了所提方法的有效性和广泛适用性。
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引用次数: 0
A Review-Level Sentiment Information Enhanced Multitask Learning Approach for Explainable Recommendation 用于可解释推荐的评论级情感信息增强型多任务学习方法
IF 4.5 2区 计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS Pub Date : 2024-04-26 DOI: 10.1109/TCSS.2024.3376728
Fenfang Xie;Yuansheng Wang;Kun Xu;Liang Chen;Zibin Zheng;Mingdong Tang
Recommendation system plays a remarkable role in solving the problem of information overload on the Internet. Existing research demonstrates that a recommended list enclosed with appropriate explanations can enhance the transparency of the system and encourage users to make decisions. Although existing works have achieved effective results, they still suffer from at least one of the following limitations: the work either does not use sentiment information or review information, does not explicitly incorporate review-level sentiment information into the model, is based on review retrieval, and generates explanations in the form of templates or phrases. To tackle the above limitations, this article proposes a REview-level Sentiment information enhanced multiTask learning approach for Explainable Recommendation (RESTER). Specifically, it first considers the user's review information and analyzes the sentiment polarity contained in the review. Then, the user/item's identity feature, review feature, and sentiment information are fused into a multitask learning framework by leveraging the implicit correlation between the rating prediction and explanation generation tasks. Comprehensive experiments on datasets in three different domains have shown that the proposed model is superior to all other baselines in both rating prediction and explanation generation tasks.
推荐系统在解决互联网信息过载问题方面发挥着重要作用。现有研究表明,附有适当解释的推荐列表可以提高系统的透明度,鼓励用户做出决策。虽然现有研究已取得了有效成果,但仍存在以下至少一个局限性:未使用情感信息或评论信息、未明确将评论级情感信息纳入模型、基于评论检索、以模板或短语形式生成解释。针对上述局限,本文提出了一种用于可解释推荐的评论级情感信息增强型多任务学习方法(RESTER)。具体来说,它首先考虑用户的评论信息,分析评论中包含的情感极性。然后,利用评分预测和解释生成任务之间的隐含相关性,将用户/项目的身份特征、评论特征和情感信息融合到多任务学习框架中。在三个不同领域的数据集上进行的综合实验表明,所提出的模型在评分预测和解释生成任务方面都优于所有其他基线模型。
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引用次数: 0
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IEEE Transactions on Computational Social Systems
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