Information Sciences最新文献_第2页

Lyapunov-based emotion-aware switching in hybrid human-artificial intelligence customer service systems 基于lyapunov的混合人-人工智能客户服务系统的情感感知切换

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-29 DOI: 10.1016/j.ins.2026.123172

Zehan Tan, Henghua Shen

{"title":"Lyapunov-based emotion-aware switching in hybrid human-artificial intelligence customer service systems","authors":"Zehan Tan, Henghua Shen","doi":"10.1016/j.ins.2026.123172","DOIUrl":"10.1016/j.ins.2026.123172","url":null,"abstract":"<div><div>In this paper, we novelly apply the classical Lyapunov stability analysis to hybrid human-Artificial Intelligence (AI) customer service systems. The core idea is to use the Lyapunov ellipsoid of a linear autonomous dynamical system (LADS) to assess the customers’ emotional states and automatically determine whether a switch from the AI agent to a human agent is necessary. This involves two innovations: 1) User emotions are modeled as discrete-time LADSs in the Pleasure–Arousal–Dominance (PAD) space, parameterized by MBTI-specific dynamics matrices; 2) A Lyapunov function defines a safe emotional ellipsoid whose boundary, together with a Lyapunov Decay Rate (LDR), forms a dual-trigger switching mechanism to transfer service from the AI agent to a human agent when the user’s real-time emotional state approaches too fast or crosses the ellipsoid boundary, thus proactively preventing emotional destabilization.</div><div>To evaluate the proposed framework, we construct a domain-specific, multi-turn customer service dialogue dataset with PAD annotations. We compare our method with three other existing customer service systems, including methods with Fixed Lyapunov Ellipsoid for All (FLEA), Rule-Based Thresholding (RBT) and No-Switching Baseline (NSB). Comparative experiments demonstrate that the proposed switching mechanism significantly improves reduces negative emotional outcomes, enhances system usability and minimizes unnecessary human intervention.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123172"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond static cues: Detecting fine-grained forgeries via temporal inconsistencies in facial dynamics 超越静态线索：通过面部动态的时间不一致性检测细粒度伪造

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-30 DOI: 10.1016/j.ins.2026.123165

Peixu Zhang , Mohan Zhang , Tongyu Wang , Xinyu Yang

{"title":"Beyond static cues: Detecting fine-grained forgeries via temporal inconsistencies in facial dynamics","authors":"Peixu Zhang , Mohan Zhang , Tongyu Wang , Xinyu Yang","doi":"10.1016/j.ins.2026.123165","DOIUrl":"10.1016/j.ins.2026.123165","url":null,"abstract":"<div><div>Fine-grained facial attribute editing represents a new frontier in deepfake technology, creating hyper-realistic forgeries that evade traditional detection methods by preserving identity and motion consistency. This subtlety poses a dual challenge: existing detectors, tuned for coarse artifacts, are rendered ineffective, and research is hampered by the absence of dedicated benchmark datasets. This paper argues that the key to unmasking these fine-grained forgeries lies not in static appearance, but in the temporal inconsistencies of underlying facial dynamics. To catalyze research in this area, we introduce EditForge, the first large-scale video dataset focused specifically on fine-grained facial attribute editing. Our analysis confirms that dynamic signals provide a powerful forensic trace that is consistently disrupted during the fine-grained forgery process. Building on this insight, we propose Fine-grained Forgery Mamba (F<sup>2</sup>-Mamba), a novel multimodal detection framework. F<sup>2</sup>-Mamba synergistically models features from facial dynamics, static appearance, and audio, employing robust alignment mechanisms and a Bi-Mamba architecture to efficiently capture long-range, cross-modal temporal dependencies. Extensive experiments validate that F<sup>2</sup>-Mamba establishes a new state-of-the-art, achieving area under the ROC curve (AUC) of 99.0% on fine-grained forgeries. This performance signals a paradigm shift towards behavior-based, dynamic analysis, significantly raising the bar for future forgery generation.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123165"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-branch Meso-Xception network for hybrid-domain feature of deepfake detection 基于混合域特征的双分支Meso-Xception网络深度伪造检测

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-27 DOI: 10.1016/j.ins.2026.123132

Yanan Song, Xiangyuan Chen, Ronghua Xu

{"title":"Dual-branch Meso-Xception network for hybrid-domain feature of deepfake detection","authors":"Yanan Song, Xiangyuan Chen, Ronghua Xu","doi":"10.1016/j.ins.2026.123132","DOIUrl":"10.1016/j.ins.2026.123132","url":null,"abstract":"<div><div>The rapid advancement of deep learning-based generative technologies has led to remarkable achievements in deepfake applications using video and image media, particularly in areas such as face swapping and expression transfer. However, these developments have also triggered significant concerns regarding media authenticity and information security. Deepfake content often exhibits various artifacts: in the spatial domain, it may suffer from over-smoothed textures, loss of edge details, or jagged distortions; in the frequency domain, abnormal peaks in high-frequency spectra or noise-induced distortions may appear; and at the semantic level, misaligned keypoints and poor temporal coherence are frequently observed. To address these limitations, this study proposes a network architecture that first performs hybrid-domain feature extraction on deepfake samples. The Xception backbone, optimized through a knowledge distillation strategy to remove redundant layers, is combined with the lightweight MesoNet4 architecture to form a dual-branch backbone that can capture semantic features at different levels. While preserving semantic representation, the overall model size is compressed to just 8.0M parameters, achieving both high-precision detection of deepfake samples (accuracy <span><math><mo>≥</mo></math></span> 99%) and real-time inference performance (single-frame latency <span><math><mo>≤</mo></math></span> 10 ms).</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123132"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel graph-theoretic and data visualization framework for spatiotemporal bubble analysis in turbulent flows 湍流中时空气泡分析的一种新的图论和数据可视化框架

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-29 DOI: 10.1016/j.ins.2026.123164

Alaa A. Najim , Safa A. Najim

{"title":"A novel graph-theoretic and data visualization framework for spatiotemporal bubble analysis in turbulent flows","authors":"Alaa A. Najim , Safa A. Najim","doi":"10.1016/j.ins.2026.123164","DOIUrl":"10.1016/j.ins.2026.123164","url":null,"abstract":"<div><div>This paper presents a novel methodology for analyzing bubble movement in turbulent flows by framing multi-object tracking as a global optimization problem on a spatiotemporal graph. Bubble detections across frames are modeled as nodes in a directed graph, with tracking addressed through a minimum cost flow algorithm. Unlike sequential trackers, our approach considers the entire temporal context to achieve globally optimal paths, reducing identity switches and fragmentation caused by occlusions and interactions. The pipeline integrates anisotropic Gaussian filtering and Otsu’s thresholding with a cost function enforcing movement and appearance consistency. Experimental validation across flow rates (<span><math><msub><mi>Q</mi><mn>1</mn></msub><mo>=</mo><mn>1.0</mn></math></span>, <span><math><msub><mi>Q</mi><mn>2</mn></msub><mo>=</mo><mn>2.0</mn></math></span>, <span><math><msub><mi>Q</mi><mn>3</mn></msub><mo>=</mo><mn>3.0</mn></math></span>) identifies distinct systems: stable homogeneous, transitional interference, and saturated aggregation-dominated. The method simultaneously measures temporal evolution of bubble count <span><math><mi>N</mi><mo>(</mo><mi>t</mi><mo>)</mo></math></span>, mean diameter <span><math><mover><mi>d</mi><mo>―</mo></mover><mo>(</mo><mi>t</mi><mo>)</mo></math></span>, and velocity <span><math><mover><mi>v</mi><mo>―</mo></mover><mo>(</mo><mi>t</mi><mo>)</mo></math></span>, revealing critical phenomena like saturation effects where increased airflow leads to bubble aggregation rather than an increased count.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123164"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DE-SHAP: A meta-optimization framework leveraging differential evolution for efficient and scalable kernel SHAP explanations DE-SHAP：一个元优化框架，利用差分进化来实现高效和可扩展的内核SHAP解释

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-22 DOI: 10.1016/j.ins.2026.123128

El Arbi Abdellaoui Alaoui , Hayat Sahlaoui , Amine Sallah , Anand Nayyar

{"title":"DE-SHAP: A meta-optimization framework leveraging differential evolution for efficient and scalable kernel SHAP explanations","authors":"El Arbi Abdellaoui Alaoui , Hayat Sahlaoui , Amine Sallah , Anand Nayyar","doi":"10.1016/j.ins.2026.123128","DOIUrl":"10.1016/j.ins.2026.123128","url":null,"abstract":"<div><div>The intractable nature of the Kernel SHAP computation presents a significant barrier to implementing rigorous explainable AI in practical large scale machine learning systems. The standard SHAP calculations are based on iterative evaluations of the model averaged over all possible feature subsets, and they become intractable for large dimensional data. To mitigate this difficulty, this paper presents “DE-SHAP”, an evolutionary optimization approach which speeds up ES-method by automatically tuning its parameters via Differential Evolution (DE). DE systematically optimizes two critical parameters background dataset size and Monte Carlo sampling count to minimize computational cost and maintain theoretical soundness within SHAP’s additive feature attribution framework. The proposed framework employs specialized evolutionary operators to ensure convergence efficiency and stability during optimization.</div><div>To test and validate the proposed methodology, extensive experiments were performed on various benchmark datasets and model architectures, and the results showed that DE-SHAP reduces computing cost by 52–97%, while the deviation of SHAP values is less than 5% and accuracy of the model remains within a range of approximately 0.5%. Given the fact that it is usually expensive to obtain explanations compared to predictions, these findings provide evidence that DE-SHAP can offer a similar quality of interpretability for a much lower computational cost. By implementing a computationally efficient theoretically justified improvement to a popular interpretability approach. DE-SHAP enables scalable and practical deployment of high-quality model explanations for complex systems with up to 784 input features. This contribution advances the feasibility of rigorous explainable AI in real-world applications, bridging the gap between interpretability research and operational machine learning.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123128"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weakly supervised object localization via frequency guidance with consistency awareness 带有一致性意识的频率引导弱监督目标定位

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-31 DOI: 10.1016/j.ins.2026.123186

Bingfeng Li , Erdong Shi , Boxiang Lv , Qingshan Chen , Jiayu Zhang , Haoran Feng , Shuai Wang

{"title":"Weakly supervised object localization via frequency guidance with consistency awareness","authors":"Bingfeng Li , Erdong Shi , Boxiang Lv , Qingshan Chen , Jiayu Zhang , Haoran Feng , Shuai Wang","doi":"10.1016/j.ins.2026.123186","DOIUrl":"10.1016/j.ins.2026.123186","url":null,"abstract":"<div><div>Weakly supervised object localization (WSOL) aims to localize object regions using only image-level labels, avoiding costly bounding box annotations. Recent methods employ foreground prediction map (FPM) to distinguish foreground from background and improve localization completeness. However, FPM-based approaches often rely on background modeling, which can introduce semantic ambiguity due to the inherent uncertainty of background regions. To address this, we propose the Frequency-Guided and Consistency-Aware (FGCA) Network, which enhances foreground modeling without relying on background modeling. FGCA first employs a Wavelet-Frequency Attention Module (WFAM) to decompose features into low- and high-frequency components, selectively enhancing semantic and fine-grained structural information. Subsequently, a consistency-aware optimization framework is introduced to enhance the semantic coherence and structural integrity of the predicted foreground by incorporating complementary constraints from both local and global perspectives. Specifically, the Semantic-Spatial Consistency Loss (SSCL) enforces fine-grained consistency by integrating category-specific discrimination and pixel-level structural smoothness. In parallel, the Foreground-Global Kullback–Leibler Alignment Loss (FG-KL) regularizes the global semantic distribution of the foreground, guiding the network to emphasize contextually relevant object regions while suppressing background-induced noise. Experiments on standard WSOL benchmarks show that FGCA outperforms state-of-the-art methods, particularly in foreground completeness, boundary precision, and semantic consistency.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123186"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PMFF-SRNet: A progressive multi-feature fusion network for hyperspectral image reconstruction PMFF-SRNet：用于高光谱图像重建的渐进式多特征融合网络

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-28 DOI: 10.1016/j.ins.2026.123129

Tianhao Yuan, Xia Wang, Qiyang Sun, Yuyang Li

{"title":"PMFF-SRNet: A progressive multi-feature fusion network for hyperspectral image reconstruction","authors":"Tianhao Yuan, Xia Wang, Qiyang Sun, Yuyang Li","doi":"10.1016/j.ins.2026.123129","DOIUrl":"10.1016/j.ins.2026.123129","url":null,"abstract":"<div><div>Hyperspectral imaging (HSI) contains abundant spatial and spectral cues, making it advantageous for a wide range of applications, including Earth observation, medicine, as well as agricultural analysis. However, conventional HSI systems are often constrained by high costs, limited acquisition efficiency, and weak adaptability to dynamic scenes. To mitigate these limitations, we propose a progressive multi-feature fusion model, termed PMFF-SRNet, for RGB-to-hyperspectral reconstruction. The proposed model progressively recovers spectral information through multiple stages, which helps reduce spectral redundancy while improving reconstruction efficiency. Furthermore, a Local–Global Spectral Attention (LGSA) module is employed to model spectral features at different granularities, where grouped self-attention focuses on local band interactions while spectral order information contributes to long-range dependency modeling. In addition, a Discrete Wavelet Attention (DWA) module is incorporated into the skip connections to enhance texture and edge restoration by exploiting the multi-scale characteristics of wavelet transforms. Results obtained on the NTIRE benchmarks indicate that PMFF-SRNet achieves competitive reconstruction performance across multiple evaluation metrics, while maintaining a lightweight and computationally efficient architecture. These findings demonstrate the strong potential of PMFF-SRNet for practical hyperspectral reconstruction tasks.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123129"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hidden policy conditional attribute-based keyword search in federated learning framework 联邦学习框架中基于隐藏策略条件属性的关键字搜索

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-01-27 DOI: 10.1016/j.ins.2026.123150

Zerui Guo , Sha Ma , Qiong Huang

{"title":"Hidden policy conditional attribute-based keyword search in federated learning framework","authors":"Zerui Guo , Sha Ma , Qiong Huang","doi":"10.1016/j.ins.2026.123150","DOIUrl":"10.1016/j.ins.2026.123150","url":null,"abstract":"<div><div>Nowadays, people are paying increasing attention to the security of personal privacy data in artificial intelligence systems. Federated learning is a solution to address the issue of unified data collection for training in artificial intelligence systems. Among these, the Cross-Sample computation process is a stage where personal privacy data is often leaked in federated learning, and a viable mechanism to ensure data confidentiality during the Cross-Sample computation process in federated learning is provided by attribute-based searchable encryption (ABSE). However, the search process in most existing ABSE systems is inherently sequential, which fundamentally precludes their use in scenarios demanding high throughput and concurrent execution. Meanwhile, it is worth noting that most ABSE schemes fail to achieve both rich attribute expression and hidden policy. In response to these limitations, the present work introduces Hidden Policy Conditional Attribute-Based Keyword Search (HP-CABKS), which supports server-side concurrent evaluation in the search phase. Under the Generic Group Model, our scheme satisfies adaptive security against chosen keyword attacks and keyword secrecy. Experimental results demonstrate that our scheme exhibits low time consumption in the search phase. Our scheme exhibits robust security, high efficiency, and strong practicality, making it well-suited for real-world applications such as Cross-Sample computation in federated learning.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123150"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Communication-efficient decentralized federated graph learning via knowledge distillation under dual heterogeneity 双异构下基于知识蒸馏的高效通信分散联邦图学习

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-02-02 DOI: 10.1016/j.ins.2026.123192

Guojiang Shen , Xu Guo , Haopeng Yuan , Jiaxin Du , Wenyi Zhang , Y. Neil Qu , Xiangjie Kong

{"title":"Communication-efficient decentralized federated graph learning via knowledge distillation under dual heterogeneity","authors":"Guojiang Shen , Xu Guo , Haopeng Yuan , Jiaxin Du , Wenyi Zhang , Y. Neil Qu , Xiangjie Kong","doi":"10.1016/j.ins.2026.123192","DOIUrl":"10.1016/j.ins.2026.123192","url":null,"abstract":"<div><div>Federated Graph Learning (FGL) integrates the privacy-preserving advantages of federated learning with the structural data modeling capabilities of graph neural networks. To mitigate single points of failure and communication bottlenecks inherent in server-based federated learning, we focus on Decentralized Federated Graph Learning (DFGL), which facilitates efficient distributed training in a peer-to-peer (P2P) manner. However, existing approaches predominantly rely on the direct exchange of gradients or model parameters. Under constrained communication resources and dual heterogeneity (data and model), such approaches often suffer from reduced training efficiency and suboptimal performance. To address these issues, we propose DFedGD, a decentralized federated graph learning framework that integrates graph condensation with knowledge distillation. Specifically, DFedGD extracts representative knowledge by synthesizing condensed subgraphs that approximate the global data distribution within a fully decentralized environment. Clients exchange and learn from condensed subgraphs and logits via a mutual knowledge distillation mechanism, thereby enhancing communication efficiency and privacy preservation while naturally supporting model heterogeneity. Furthermore, an alignment mechanism based on shared anchor samples is incorporated to enforce latent representation consistency, effectively mitigating domain shifts arising from non-IID data distributions. Extensive experiments on three publicly available datasets demonstrate that our framework outperforms competitive baselines.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123192"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SWGCN: Synergy weighted graph convolutional network for multi-behavior recommendation SWGCN：多行为推荐的协同加权图卷积网络

IF 6.8 1区计算机科学

Information Sciences Pub Date : 2026-05-25 Epub Date: 2026-02-03 DOI: 10.1016/j.ins.2026.123177

Fangda Chen, Yueyang Wang, Chaoli Lou, Min Gao, Qingyu Xiong

{"title":"SWGCN: Synergy weighted graph convolutional network for multi-behavior recommendation","authors":"Fangda Chen, Yueyang Wang, Chaoli Lou, Min Gao, Qingyu Xiong","doi":"10.1016/j.ins.2026.123177","DOIUrl":"10.1016/j.ins.2026.123177","url":null,"abstract":"<div><div>Multi-behavior recommendation paradigms have emerged to capture diverse user activities, forecasting primary conversions (e.g., purchases) by leveraging secondary signals like browsing history. However, current graph-based methods often overlook cross-behavioral synergistic signals and the fine-grained intensity of individual actions. Motivated by the need to overcome these shortcomings, we introduce <strong>S</strong>ynergy <strong>W</strong>eighted <strong>G</strong>raph <strong>C</strong>onvolutional <strong>N</strong>etwork (<strong>SWGCN</strong>). SWGCN introduces two novel components: a Target Preference Weigher, which adaptively assigns weights to user-item interactions within each behavior, and a Synergy Alignment Task, which guides its training by leveraging an Auxiliary Preference Valuator. This task prioritizes interactions from synergistic signals that more accurately reflect user preferences. The performance of our model is rigorously evaluated through comprehensive tests on three open-source datasets, specifically Taobao, IJCAI, and Beibei. On the Taobao dataset, SWGCN yields relative gains of 112.49% and 156.36% in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG), respectively. It also yields consistent gains on IJCAI and Beibei, confirming its robustness and generalizability across various datasets. Our implementation is open-sourced and can be accessed via <span><span>https://github.com/FangdChen/SWGCN</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123177"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0