Information Processing & Management最新文献_第3页

Self-supervised star graph optimization embedding non-negative matrix factorization 嵌入非负矩阵因式分解的自监督星图优化

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2024-11-18 DOI: 10.1016/j.ipm.2024.103969

Songtao Li , Qiancheng Wang , MengJie Luo , Yang Li , Chang Tang

{"title":"Self-supervised star graph optimization embedding non-negative matrix factorization","authors":"Songtao Li , Qiancheng Wang , MengJie Luo , Yang Li , Chang Tang","doi":"10.1016/j.ipm.2024.103969","DOIUrl":"10.1016/j.ipm.2024.103969","url":null,"abstract":"<div><div>Labeling expensive and graph structure fuzziness are recognized as indispensable prerequisites for solving practical problems in semi-supervised graph learning. This paper proposes a novel approach: a non-negative matrix factorization algorithm based on self-supervised star graph optimal embedding, utilizing the progressive spontaneous strategy of anchor graphs. The model considers the feature assignment rules in unlabeled samples and constructs a corresponding probabilistic extension model to extract pseudo-labeled information from the samples. It also constructs self-supervised hard constraints accordingly to enhance the learning process. In addition, inspired by the graph structure filter, we propose a star graph optimization method. It smooths the association relationships between nodes in the graph structure and improves the accuracy of the graph regularization term in describing the association relationships of the original data. Finally, we give the objective function of the model with the multiplicative update rule and analyze the convergence of the algorithm under this rule. Clustering experiments on several standard image datasets and electroencephalography datasets show that the proposed algorithm improves over the current state-of-the-art benchmark algorithms by 6.9% on average. This indicates that the proposed model has excellent self-supervised label discovery and data representation capabilities.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 2","pages":"Article 103969"},"PeriodicalIF":7.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploiting multiple influence pattern of event organizer for event recommendation 利用活动组织者的多重影响模式进行活动推荐

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2024-11-18 DOI: 10.1016/j.ipm.2024.103966

Xiaofeng Han, Xiangwu Meng, Yujie Zhang

{"title":"Exploiting multiple influence pattern of event organizer for event recommendation","authors":"Xiaofeng Han, Xiangwu Meng, Yujie Zhang","doi":"10.1016/j.ipm.2024.103966","DOIUrl":"10.1016/j.ipm.2024.103966","url":null,"abstract":"<div><div>Existing event recommendation methods pay attention to contextual factors to approach sparse and cold-start problem, in which organizer influence is a vital factor in Event-based Social Networks (EBSNs). However, existing studies ignore multiple influence pattern of organizer at event-level. In this paper, we distinguish organizer role and user (participant) role, exploring the organizer multiple influence pattern at event-level based on two scores: organizer behavior score and organizer popularity score. Besides, the organizer influence at event-level is dynamic, the step length is the time difference between two adjacent events from same organizer. Based on this discovery, we first calculate the organizer behavior score and organizer popularity score, then we propose an Organizer Multiple Influence Pattern-aware model (OMIP) based on topic model to capture user event topic preferences under the multiple influence pattern, which models the correlation and alternative-relation between user behavior topic and influence pattern. OMIP depends on the user’s participation records, user’s profiles and organizer’s profiles. OMIP outperforms state-of-the-art baselines with remarkable improvements in terms of Recall@k, NDCG@k, F1@k, and AUC. Specifically, Recall@5 improvement of 0.22%–16.41%; NDCG@5 improvement of 1.25%–10.81%; F1@5 improvement of 3.49%–16.43%; AUC improvement of 0.70%–1.62% on two real-world EBSNs datasets. Besides, OMIP can learn semantically topics and patterns which are useful to explain recommendations.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 2","pages":"Article 103966"},"PeriodicalIF":7.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wanting information: Uncertainty and its reduction through search engagement 想要信息：不确定性以及通过参与搜索减少不确定性

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2024-11-18 DOI: 10.1016/j.ipm.2024.103890

Frans van der Sluis

引用次数: 0

Unsupervised Adaptive Hypergraph Correlation Hashing for multimedia retrieval 用于多媒体检索的无监督自适应超图相关性哈希算法

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2024-11-18 DOI: 10.1016/j.ipm.2024.103958

Yunfei Chen , Yitian Long , Zhan Yang , Jun Long

{"title":"Unsupervised Adaptive Hypergraph Correlation Hashing for multimedia retrieval","authors":"Yunfei Chen , Yitian Long , Zhan Yang , Jun Long","doi":"10.1016/j.ipm.2024.103958","DOIUrl":"10.1016/j.ipm.2024.103958","url":null,"abstract":"<div><div>Cross-modal hashing has attracted widespread attention from researchers due to its capabilities to handle large volumes of heterogeneous multimedia information with fast retrieval speed and low storage cost. However, current cross-modal hashing methods still face issues such as incomplete embedding of semantic correlation information and long parameter tuning cycles. To address these problems, we propose a method called Unsupervised Adaptive Hypergraph Correlation Hashing (UAHCH). First, the hypergraph-based correlation enhanced hashing constructs a hypergraph based on semantic information and correlation information, leveraging a hypergraph neural network to integrate the hypergraph information into the hash codes, ensuring the richness of the semantics and the integrity of correlation relationships. Next, the fast parameter adaptive strategy is designed for the automated optimization of neural network parameters for the UAHCH method and various neural network models, achieving optimal performance more efficiently. Finally, comprehensive experiments are conducted on widely used datasets. The results show that the proposed UAHCH method achieves superior performance, with average improvements of 3.06% on MIRFlickr, 1.45% on NUS-WIDE, and 4.65% on MSCOCO compared to the latest baseline methods. The code has been made publicly available at <span><span>https://github.com/YunfeiChenMY/UAHCH</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 2","pages":"Article 103958"},"PeriodicalIF":7.4,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing robustness in implicit feedback recommender systems with subgraph contrastive learning 利用子图对比学习增强隐式反馈推荐系统的鲁棒性

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2024-11-16 DOI: 10.1016/j.ipm.2024.103962

Yi Yang , Shaopeng Guan , Xiaoyang Wen

{"title":"Enhancing robustness in implicit feedback recommender systems with subgraph contrastive learning","authors":"Yi Yang , Shaopeng Guan , Xiaoyang Wen","doi":"10.1016/j.ipm.2024.103962","DOIUrl":"10.1016/j.ipm.2024.103962","url":null,"abstract":"<div><div>Contrastive learning operates by distinguishing differences between various nodes to facilitate item recommendations. However, current graph contrastive learning (GCL) methods suffer from insufficient robustness. To mitigate the impact of noise and accurately capture user preferences, we propose a subgraph-based GCL method: SubGCL. Firstly, we devise a dynamic perceptual signal extractor that leverages node degree and neighborhood information to model subgraphs corresponding to nodes and compute mutual information scores. This approach enhances view adaptivity, thereby improving data augmentation robustness against noise perturbations. Secondly, we develop an association graph self-attention propagation mechanism. This mechanism constructs node clusters by randomly sampling nodes and edges, facilitating self-attention propagation on the graph to learn cluster associations and enhance recommendation accuracy. Finally, we reconstruct graph structures through recommendation loss and update node embeddings via contrastive learning to bolster the model’s accuracy and robustness in implicit feedback data. We conducted experiments on three publicly available real-world datasets. Results demonstrate that, compared to existing contrastive learning recommendation approaches, SubGCL achieves an average improvement of 4.96% and 3.98% in Recall and NDCG metrics, respectively.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103962"},"PeriodicalIF":7.4,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Patients' cognitive and behavioral paradoxes in the process of adopting conflicting health information: A dynamic perspective 患者在接受相互矛盾的健康信息过程中的认知和行为悖论：动态视角

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2024-11-15 DOI: 10.1016/j.ipm.2024.103939

Yan Jin , Di Zhao , Zhuo Sun , Chongwu Bi , Ruixian Yang , Shengli Deng

{"title":"Patients' cognitive and behavioral paradoxes in the process of adopting conflicting health information: A dynamic perspective","authors":"Yan Jin , Di Zhao , Zhuo Sun , Chongwu Bi , Ruixian Yang , Shengli Deng","doi":"10.1016/j.ipm.2024.103939","DOIUrl":"10.1016/j.ipm.2024.103939","url":null,"abstract":"<div><div>Diversified access to health information has increased the likelihood of encountering conflicting health messages, making it more difficult for patients to adopt information rationally. Prior research has primarily focused on the outcomes of patients' information adoption and responded to concerns by exploring the influences that led to these outcomes, overlooking a crucial aspect. Specifically, patients' cognitive and behavioral responses are continuously fluctuating during the process of information adoption. A total of 336 subjects (valid sample) participated in this study. A combination of situational experiments, grounded theory, and questionnaires was employed to develop a model of patients' adoption of conflicting health information. The concept of \"trans-theory\" was introduced to explain how patients' cognitive and behavioral responses changed at different segments of adoption. In contrast to prior studies viewing information adoption as a whole, we propose that the process can be divided into four distinct segments: information attention, comprehension, evaluation, and decision. Moreover, the sequential influence of information, ability, psychological, and environmental factors in the adoption process produces three common paradoxes in patients' cognitive and behavioral responses, affecting their ability to make rational adoption decisions. This study explores the dynamics of information adoption from the patient's perspective, providing novel insights into the study of conflicting health information adoption and offering guidance for designing more effective interventions for facilitating rational adoption by patients. Additionally, it can help the healthcare system better understand patients' cognitive and behavioral responses to deliver more effective healthcare services.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103939"},"PeriodicalIF":7.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Domain disentanglement and fusion based on hyperbolic neural networks for zero-shot sketch-based image retrieval 基于双曲神经网络的基于零镜头草图的图像检索的域分解和融合

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2024-11-15 DOI: 10.1016/j.ipm.2024.103963

Qing Zhang , Jing Zhang , Xiangdong Su , Yonghe Wang , Feilong Bao , Guanglai Gao

{"title":"Domain disentanglement and fusion based on hyperbolic neural networks for zero-shot sketch-based image retrieval","authors":"Qing Zhang , Jing Zhang , Xiangdong Su , Yonghe Wang , Feilong Bao , Guanglai Gao","doi":"10.1016/j.ipm.2024.103963","DOIUrl":"10.1016/j.ipm.2024.103963","url":null,"abstract":"<div><div>With the advancement of zero-shot sketch-based image retrieval (ZS-SBIR) tasks, existing methods still encounter two major challenges: Euclidean space fails to effectively represent data with hierarchical structures, leading to non-discriminative retrieval features; relying solely on visual information is insufficient to align cross-domain features and maximize their domain generalization capabilities. To tackle these issues, this paper designs a hyperbolic neural networks based ZS-SBIR framework that considers domain disentanglement and fusion learning, called “DDFUS”. Specifically, we present a contrastive cross-modal learning method that guides the alignment of multi-domain visual representations with semantic representations in the hyperbolic space. This approach ensures that each visual representation possesses rich semantic hierarchical structure information. Furthermore, we propose a domain disentanglement method based on hyperbolic neural networks that employs paired hyperbolic encoders to decompose the representation of each domain into domain-invariant and domain-specific features to reduce information disturbance between domains. Moreover, we design an advanced cross-domain fusion method that promotes the fusion and exchange of multi-domain information through the reconstruction and generation of cross-domain samples. It significantly enhances the representation and generalization capabilities of domain-invariant features. Comprehensive experiments demonstrate that the mAP@all of our DDFUS model surpasses CNN-based models by 18.99 % on the Sketchy dataset, 1.93 % on the more difficult TU-Berlin dataset, and 11.4 % on the more challenging QuickDraw dataset.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103963"},"PeriodicalIF":7.4,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing video rumor detection through multimodal deep feature fusion with time-sync comments 通过多模态深度特征融合与时间同步评论加强视频谣言检测

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2024-11-14 DOI: 10.1016/j.ipm.2024.103935

Ming Yin , Wei Chen , Dan Zhu , Jijiao Jiang

{"title":"Enhancing video rumor detection through multimodal deep feature fusion with time-sync comments","authors":"Ming Yin , Wei Chen , Dan Zhu , Jijiao Jiang","doi":"10.1016/j.ipm.2024.103935","DOIUrl":"10.1016/j.ipm.2024.103935","url":null,"abstract":"<div><div>Rumors in videos have a stronger propagation compared to traditional text or image rumors. Most current studies on video rumor detection often rely on combining user and video modal information while neglecting the internal multimodal aspects of the video and the relationship between user comments and local segment of the video. To address this problem, we propose a method called Time-Sync Comment Enhanced Multimodal Deep Feature Fusion Model (TSC-MDFFM). It introduces time-sync comments to enhance the propagation structure of videos on social networks, supplementing missing contextual or additional information in videos. Time-sync comments focus on expressing users' views on specific points in time in the video, which helps to obtain more valuable segments from videos with high density information. The time interval from one keyframe to the next in a video is defined as a local segment. We thoroughly described this segment using time-sync comments, video keyframes, and video subtitle texts. The local segment sequences are ordered based on the video timeline and assigned time information, then fused to create the local feature representation of the video. Subsequently, we fused the text features, video motion features, and visual features of video comments at the feature level to represent the global features of the video. This feature not only captures the overall propagation trend of video content, but also provides a deep understanding of the overall features of the video. Finally, we will integrate local and global features for video rumor classification, to combine the local and global information of the video. We created a dataset called TSC-VRD, which includes time-sync comments and encompasses all visible information in videos. Extensive experimental results have shown superior performance of our proposed model compared to existing methods on the TSC-VRD dataset.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103935"},"PeriodicalIF":7.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study of technology communities and dominant technology lock-in in the Internet of Things domain - Based on social network analysis of patent network 物联网领域的技术社群和主导技术锁定研究--基于专利网络的社会网络分析

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2024-11-14 DOI: 10.1016/j.ipm.2024.103959

Xueting Yang , Bing Sun , Shilong Liu

{"title":"Study of technology communities and dominant technology lock-in in the Internet of Things domain - Based on social network analysis of patent network","authors":"Xueting Yang , Bing Sun , Shilong Liu","doi":"10.1016/j.ipm.2024.103959","DOIUrl":"10.1016/j.ipm.2024.103959","url":null,"abstract":"<div><div>The evolution of technology communities and the lock-in process of dominant technologies influence the advancement of the Internet of Things (IoT) in achieving its goal of connecting everything. This study aims to identify and analyze IoT technology communities and main technology trajectories, and to trace and explore the lock-in and unlocking process of IoT dominant technologies. A directed citation network was constructed using 9,464 IoT patent families as nodes and 23,604 inter-patent citation relationships as directed links. We used the Louvain algorithm and Latent Dirichlet Allocation (LDA) modeling technique to divide the communities and extract their themes, and the SPLC algorithm and key-route global main path search method to identify the dominant technology trajectories. The results show that first, technologies that emerged during the embryonic stage of IoT exhibit a declining trend as the standardization process of IoT progresses; technologies introduced during IoT's growing stage continue to increase, benefiting from the positive cyclical effect of application and integrated innovation. Second, major developments in IoT involve device risk assessment, machine learning, and machine-to-machine technologies. Third, the lock-in of IoT dominant technologies is accompanied by a 'learning by using' effect and an incremental succession of innovations. The novelty of this study lies in the combination of both community analysis and main path analysis methods, which help researchers and participators grasp the IoT technology development holistically from both horizontal - technology categorization and vertical - time perspectives. Meanwhile, we also analyzed the lock-in and unlocking process of IoT dominant technologies to provide a reference for participators to develop technological strategies.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103959"},"PeriodicalIF":7.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metaverse-based distance learning as a transactional distance mitigator and memory retrieval stimulant 基于元数据的远程学习是一种交易距离缓解剂和记忆检索刺激剂

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2024-11-11 DOI: 10.1016/j.ipm.2024.103957

Cheong Kim , Francis Joseph Costello , Jungwoo Lee , Kun Chang Lee

{"title":"Metaverse-based distance learning as a transactional distance mitigator and memory retrieval stimulant","authors":"Cheong Kim , Francis Joseph Costello , Jungwoo Lee , Kun Chang Lee","doi":"10.1016/j.ipm.2024.103957","DOIUrl":"10.1016/j.ipm.2024.103957","url":null,"abstract":"<div><div>This present study explores Metaverse-based Distance Learning (MDL) as a mitigative strategy of transactional distance (TD) and an enhancer of memory retrieval in an educational setting. We conducted two experimental studies. In the first study (<em>n</em> = 367 participants), we found that MDL significantly reduced perceived TD, leading to positive learner attitudes and increased intentions for repeat learning. The second study utilized functional Near-Infrared Spectroscopy (fNIRS) to assess hemodynamic responses in the prefrontal cortex of 30 participants, comparing brain activity during lectures in MDL and e-learning environments. Results indicated that MDL elicited higher oxy-Hb activation in the prefrontal cortex, particularly during cognitively challenging tasks, correlating with improved memory retrieval. Grounded in both Transactional Distance Theory (TDT) and context-dependent memory (CDM) frameworks, we found that the technological and educational potential of MDL not only reduces psychological barriers in distance learning but also shows how it can improve cognitive engagement and retention. These findings underscore the potential of MDL in distance education and suggest pathways for future research to explore its implications further, particularly in conjunction with other emerging technologies.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103957"},"PeriodicalIF":7.4,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0