Information Processing & Management最新文献_第2页

Efficient processing of k-hop reachability queries on temporal bipartite graphs 时间二部图上k-hop可达性查询的高效处理

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-04-16 DOI: 10.1016/j.ipm.2025.104178

Junfeng Zhou , Zuyong Wang , Yuting Tan , Ming Du , Ziyang Chen , Xian Tang

{"title":"Efficient processing of k-hop reachability queries on temporal bipartite graphs","authors":"Junfeng Zhou , Zuyong Wang , Yuting Tan , Ming Du , Ziyang Chen , Xian Tang","doi":"10.1016/j.ipm.2025.104178","DOIUrl":"10.1016/j.ipm.2025.104178","url":null,"abstract":"<div><div>Given a temporal bipartite graph, the <span><math><mi>k</mi></math></span>-hop reachability query is used to determine whether there exists a path between two vertices in the graph that satisfies both time and length constraints. The <span><math><mi>k</mi></math></span>-hop reachability queries on temporal bipartite graphs can be used in various scenarios to facilitate data analysis, such as epidemic prevention and control and information dissemination, etc. For <span><math><mi>k</mi></math></span>-hop reachability queries processing on temporal bipartite graphs, existing methods suffer from two problems: (1) false-negative problem, which means that for some reachable queries, existing approaches return unreachable results; (2) lack of support for length constraint. To tackle the above problems, we first analyze the essential reasons of false-negative problem, and propose a traversal-based strategy to avoid the false-negative problem. To improve the efficiency, we propose a graph transform based approach to reduce the cost of graph traversal operation. We then propose to construct a compact index based on the transformed graph, which covers both time and length constraints of all vertex pairs, such that to avoid the expensive graph traversal operation. We further propose efficient algorithms to update the index when the temporal bipartite graph changes. Finally, we conduct rich experiments on real-world datasets. The experimental results show that, our methods completely avoid false-negative problem, and the query efficiency of our index-based method is more than three orders of magnitude faster than the online approach.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104178"},"PeriodicalIF":7.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835301","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

A weighted heterogeneous graph attention network method for purchase prediction of potential consumers with Multibehaviors 多行为潜在消费者购买预测的加权异构图注意网络方法

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-04-16 DOI: 10.1016/j.ipm.2025.104175

Bin Yu , Jing Zhang , Yu Fu , Zeshui Xu

{"title":"A weighted heterogeneous graph attention network method for purchase prediction of potential consumers with Multibehaviors","authors":"Bin Yu , Jing Zhang , Yu Fu , Zeshui Xu","doi":"10.1016/j.ipm.2025.104175","DOIUrl":"10.1016/j.ipm.2025.104175","url":null,"abstract":"<div><div>In the e-commerce environment, predicting the purchasing intention of potential consumers is an important component of recommendation systems, which provides a basis for personalized recommendations by predicting whether users are likely to purchase a certain product. This accurate prediction not only enables businesses to cater to consumers’ needs and preferences, thereby stimulating purchases, but also guides promotion and advertising efforts. However, most current research uses a single data structure, which may have certain limitations in improving prediction accuracy. Therefore, in order to construct a more effective purchase prediction method, this study constructs a weighted heterogeneous graph attention method based on various interaction behaviors between users and products. Firstly, we construct a multi-behavior bipartite graph based on user–product interaction behavior. Next, the user–product multi-behavior bipartite graph is reconstructed into user relationship graph and user–product relationship graph. Then, we use multi-head graph attention network to learn the neighbor node information in user relationship graph and user–product relationship graph respectively. Finally, we utilize a linear attention mechanism to automatically learn the importance of different relationship graphs in predicting user purchase intention. The effectiveness and superiority of our method is confirmed by the comparative and ablation studies conducted on the dataset of potential consumer purchase behavior provided by JD.com. Specifically, after training and parameter optimization, our method is able to achieve a precision of 0.965, a recall of 0.974, and an f1-score of 0.969, which all outperform the comparison methods.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104175"},"PeriodicalIF":7.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834706","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

Understanding gender differences in online protective products purchases through an impression management perspective: Evidence from a natural experiment 从印象管理的角度理解在线防护产品购买的性别差异：来自自然实验的证据

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-04-16 DOI: 10.1016/j.ipm.2025.104181

Zishun Qian , Jianbin Li , Yicheng Zhang , Yi Liu , Qi Wang

{"title":"Understanding gender differences in online protective products purchases through an impression management perspective: Evidence from a natural experiment","authors":"Zishun Qian , Jianbin Li , Yicheng Zhang , Yi Liu , Qi Wang","doi":"10.1016/j.ipm.2025.104181","DOIUrl":"10.1016/j.ipm.2025.104181","url":null,"abstract":"<div><div>The emergence of various health crises has led to a surge in purchases of protective products, which have been crucial in the post-pandemic era. Meanwhile, significant gender-based differences have emerged in the purchases of various protective products. Drawn on impression management theory, this study posits that individuals’ impression management concerns regarding their gender identity can help explain these differences. To examine our hypotheses, we leveraged multiple waves of COVID-19 as external shocks and analyzed transaction records on medical goods before and after the shocks using a natural experiment approach. Using data from a leading pharmaceutical e-commerce platform in China, our findings indicate that, after the pandemic outbreak, gender gaps in purchases of protective products intended for public settings become more prominent compared to those intended for private settings. Additionally, such differences are amplified for the products with higher levels of protection intensity or among individuals with infrequent coupon usage or customer service consultation. Our findings contribute to the understanding of impression management theory in explaining individuals’ responses toward protective measures during health crises. Our study also provides important practical implications for public health policies and communication strategies on contagious pandemics.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104181"},"PeriodicalIF":7.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839180","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

Multi-SEA: Multi-stage Semantic Enhancement and Aggregation for image–text retrieval Multi-SEA：用于图像文本检索的多阶段语义增强和聚合技术

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-04-14 DOI: 10.1016/j.ipm.2025.104165

Zijing Tian , Zhonghong Ou , Yifan Zhu , Shuai Lyu , Hanyu Zhang , Jinghua Xiao , Meina Song

{"title":"Multi-SEA: Multi-stage Semantic Enhancement and Aggregation for image–text retrieval","authors":"Zijing Tian , Zhonghong Ou , Yifan Zhu , Shuai Lyu , Hanyu Zhang , Jinghua Xiao , Meina Song","doi":"10.1016/j.ipm.2025.104165","DOIUrl":"10.1016/j.ipm.2025.104165","url":null,"abstract":"<div><div>Image–text retrieval aims to find a general embedding space to semantically align cross-modal tokens. Existing studies struggle to adequately integrate information cross different modality encoders and usually neglect implicit semantic information mining, making it difficult to accurately understand and represent cross-modal information. To resolve the problems mentioned above, we propose a Multi-stage Semantic Enhancement and Aggregation framework (<strong>Multi-SEA</strong>) with novel networks and training schemes, which can more comprehensively integrate global and local information within both intra-modal and inter-modal features. Multi-SEA first designs a fusion module with agent attention and gating mechanism. It helps the model focus on crucial information. Multi-SEA then introduces a three-stage scheme to enhance uni-modal information and aggregates fine-grained cross-modal information by involving the fusion module in different stages. Eventually, Multi-SEA utilizes a negative sample queue and hierarchical scheme to facilitate robust contrastive learning and promote expressive capabilities from implicit information. Experimental results demonstrate that Multi-SEA significantly outperforms the state-of-the-art schemes, achieving notable improvements in image-to-text and text-to-image retrieval tasks on the Flickr30k, MSCOCO(1K), and MSCOCO(5K) datasets, with Recall@sum increased by 13.3, 2.8, and 4.7, respectively.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104165"},"PeriodicalIF":7.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829272","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

Personal information organization literacy in the academic context: Scale development, performance assessment, and influence exploration 学术背景下的个人信息组织素养：量表开发、绩效评估与影响探索

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-04-14 DOI: 10.1016/j.ipm.2025.104166

Gaohui Meng, Chang Liu

{"title":"Personal information organization literacy in the academic context: Scale development, performance assessment, and influence exploration","authors":"Gaohui Meng, Chang Liu","doi":"10.1016/j.ipm.2025.104166","DOIUrl":"10.1016/j.ipm.2025.104166","url":null,"abstract":"<div><div>Personal information management literacy (PIML) is a literacy that has been increasingly valued and highlighted in the emerging knowledge society, yet the related research is insufficient. This study focuses on personal information organization literacy (PIOL) as an essential component of PIML, examining its measurement, assessment, and influence in the academic context. We conducted two linked studies to address the research questions. In Study 1, we developed a scale to measure PIOL in the academic context through three phases: generating a sample of items, exploring the factorial structure, and examining the reliability and validity. In Study 2, based on the developed scale, we assessed the performance of college students in PIOL and explored the influence of PIOL on their learning performance. The results indicate that PIOL in the academic context is a five-dimensional construct. There is a gap between college students’ real performance and the ideal level of PIOL in the academic context, and their PIOL performance differ significantly, allowing them to be categorized into four groups. Moreover, it is verified that college students’ PIOL has a beneficial effect on their learning performance, including mitigating procrastination, alleviating passive procrastination, and elevating academic grades. This study takes a pioneering step in measuring PIOL and discovering its effect, with the potential to inspire educators to incorporate more PIOL elements into information literacy standards and to define PIOL education.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104166"},"PeriodicalIF":7.4,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829273","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

Tourism demand point-interval forecasting using global–local information extraction network 基于全局-局部信息抽取网络的旅游需求点区间预测

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-04-09 DOI: 10.1016/j.ipm.2025.104161

Wenzheng Liu , Hongtao Li , Haina Zhang , Shaolong Sun , Zhipeng Huang , Wuzhi Xie

{"title":"Tourism demand point-interval forecasting using global–local information extraction network","authors":"Wenzheng Liu , Hongtao Li , Haina Zhang , Shaolong Sun , Zhipeng Huang , Wuzhi Xie","doi":"10.1016/j.ipm.2025.104161","DOIUrl":"10.1016/j.ipm.2025.104161","url":null,"abstract":"<div><div>The forward-looking estimation of tourism demand is pivotal for effective resource allocation and strategic planning, making robust predictive models valuable decision-making tools. Current research predominantly emphasizes isolated types of information, often neglecting comprehensive integration of spatiotemporal data and auxiliary factors, as well as the critical role of interval forecasting in decision support. To address these gaps, we propose the Global–Local Information Extraction Network (GLIEN), a novel deep learning model for point-interval forecasting. GLIEN dynamically integrates spatiotemporal information and auxiliary factors at a global level, followed by precise analysis of each city within the global information through the designed local blocks. During the information integration phase, enhanced by an error correction mechanism, the model generates both point and interval forecasts under global information adjustments. To mitigate challenges associated with small sample sizes in tourism datasets, our proposed K-fold training strategy enhances the model’s capacity to absorb data diversity to some extent. Empirical analysis of the Hainan and Hawaii datasets demonstrates that the GLIEN outperforms all benchmark models in both point and interval forecasting across different forecast horizons. Results also highlight the error correction strategy’s role in refining interval coverage and bandwidth, while the K-fold training significantly boosts forecasting accuracy. This research offers critical insights for tourism resource management and planning, marking the first attempt at point-interval forecasting for tourism demand.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104161"},"PeriodicalIF":7.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807265","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

Classification and severity assessment of disaster losses based on multi-modal information in social media 基于社交媒体多模态信息的灾害损失分类与严重程度评估

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-04-09 DOI: 10.1016/j.ipm.2025.104179

Wei Zhou , Lu An , Ruilian Han , Gang Li

{"title":"Classification and severity assessment of disaster losses based on multi-modal information in social media","authors":"Wei Zhou , Lu An , Ruilian Han , Gang Li","doi":"10.1016/j.ipm.2025.104179","DOIUrl":"10.1016/j.ipm.2025.104179","url":null,"abstract":"<div><div>Capture and fine-grained classification of disaster loss information, combined with severity assessment are essential for emergency management departments to carry out effective emergency rescue measures. With the rapid development of social media platforms, the vast amount of user posts on social media provides critical disaster loss information during disasters. In this study, a fine-grained multimodal information-based disaster losses classification (MIDLC) model is proposed to identify the different types of disaster losses from massive social media data. This model uses three datasets, i.e., microblogging posts, government announcements about public events, and microblogging images. The disaster losses are divided into five types, i.e., casualties, houses and buildings collapse, municipal infrastructure damage, public service facilities damage, and impact on production and daily activities. Subsequently, this study proposes a disaster loss severity assessment system to evaluate the severity of different types of disaster loss reflected by social media, guiding targeted rescue response activities. The severity assessment system measures the severity from four dimensions: event information characteristics, information dissemination strength, official response, and user emotional volatility. Finally, the proposed MIDLC model and the severity assessment system are illustrated by investigating three disaster events. Results show that the MIDLC model proposed in this study significantly improves the performance of disaster losses classification. In these five disaster loss types, positive correlation exists between the casualty losses and houses and buildings collapse losses.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104179"},"PeriodicalIF":7.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800221","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

Continual and wisdom learning for federated learning: A comprehensive framework for robustness and debiasing 联邦学习的持续和智慧学习：鲁棒性和去偏性的综合框架

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-04-09 DOI: 10.1016/j.ipm.2025.104157

Saeed Iqbal , Xiaopin Zhong , Muhammad Attique Khan , Zongze Wu , Dina Abdulaziz AlHammadi , Weixiang Liu , Imran Arshad Choudhry

{"title":"Continual and wisdom learning for federated learning: A comprehensive framework for robustness and debiasing","authors":"Saeed Iqbal , Xiaopin Zhong , Muhammad Attique Khan , Zongze Wu , Dina Abdulaziz AlHammadi , Weixiang Liu , Imran Arshad Choudhry","doi":"10.1016/j.ipm.2025.104157","DOIUrl":"10.1016/j.ipm.2025.104157","url":null,"abstract":"<div><div>Federated Learning (FL) has transformed decentralized machine learning, however it remains has concerns with noisy labeled data, diverse clients, and sparse datasets, especially in delicate fields like healthcare. To address these issues, this study introduces a robust FL framework that integrates advanced Continual Learning (CL) and Wisdom Learning (WL) techniques. Elastic Weight Consolidation (EWC) prevents catastrophic forgetting by penalizing deviations from critical weights, while Progressive Neural Networks (PNN) leverage modular architectures with lateral connections to enable knowledge transfer across tasks and isolate client-specific biases. WL incorporates consensus-based aggregation, dynamic model distillation, and adaptive ensemble learning to enhance model robustness against noisy updates and biased data distributions. The framework is rigorously validated on benchmark medical imaging datasets, including ADNI, BraTS, PathMNIST, BreastMNIST, and ChestMNIST, demonstrating significant improvements in fairness metrics, with up to a 94.3% reduction in bias (Demographic Parity) and a 92.7% improvement in accuracy fairness (Accuracy Parity). These results establish the effectiveness of the proposed approach in achieving stable, equitable, and high-performing global models under challenging FL conditions characterized by dynamic client settings, label noise, and class imbalance.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104157"},"PeriodicalIF":7.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800677","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

I2QD: Unsupervised feature selection via information quality, quantity, and difference degree I2QD：基于信息质量、数量和差异程度的无监督特征选择

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-04-09 DOI: 10.1016/j.ipm.2025.104173

Pengfei Zhang , Yuxin Zhao , Lvhui Hu , Dexian Wang , Lilan Peng , Zhong Li , Herwig Unger , Tianrui Li

{"title":"I2QD: Unsupervised feature selection via information quality, quantity, and difference degree","authors":"Pengfei Zhang , Yuxin Zhao , Lvhui Hu , Dexian Wang , Lilan Peng , Zhong Li , Herwig Unger , Tianrui Li","doi":"10.1016/j.ipm.2025.104173","DOIUrl":"10.1016/j.ipm.2025.104173","url":null,"abstract":"<div><div>In the era of big data, datasets often contain a large number of features with great uncertainty and ambiguity, which makes it challenging to identify features of value in downstream tasks. Traditional unsupervised feature selection methods struggle to effectively handle uncertain or fuzzy information, as they often treat information quality and information quantity separately, leading to suboptimal feature selection. To address this limitation, we propose a novel information representation system that integrates fuzzy relations with information source values, enabling a unified framework for quantifying both the quality and quantity of information. Within this system, we introduce two key feature selection criteria: the information evaluation score (IES), which assesses the quality and quantity of information, and the difference degree (DD), which measures the difference between selected and unselected features. Based on these criteria, we develop an unsupervised feature selection algorithm that accounts for the <u>I</u>nformation <u>Q</u>uantity, <u>Q</u>uality and <u>D</u>ifference <u>D</u>egree of feature (I2QD). The I2QD algorithm effectively selects features by balancing information quality, quantity, and difference, even in the presence of uncertainty. Finally, experimental findings support the efficacy of our proposed I2QD algorithm, offering a promising solution for feature selection.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104173"},"PeriodicalIF":7.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800678","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

Distribution-guided Graph Learning for Sequential Recommendation 序列推荐的分布引导图学习

IF 7.4 1区管理学

Information Processing & Management Pub Date : 2025-04-09 DOI: 10.1016/j.ipm.2025.104119

Kaiwei Xu , Yongquan Fan , Jing Tang , Xianyong Li , Yajun Du , Xiaomin Wang

{"title":"Distribution-guided Graph Learning for Sequential Recommendation","authors":"Kaiwei Xu , Yongquan Fan , Jing Tang , Xianyong Li , Yajun Du , Xiaomin Wang","doi":"10.1016/j.ipm.2025.104119","DOIUrl":"10.1016/j.ipm.2025.104119","url":null,"abstract":"<div><div>Sequential recommendations predict the next item by capturing behavioral patterns from a user’s sequence. Graph neural networks (GNN) have recently gained popularity in sequential recommendation for effectively capturing high-order information, which notably improves recommendation performance. However, some existing GNN-based methods represent the node embeddings in the graph as fixed vectors, which fails to capture the uncertainty generated by the transition of relations between nodes. To cope with the above challenge, we propose <u><strong>D</strong></u>istribution-guided <u><strong>G</strong></u>raph <u><strong>L</strong></u>earning for <u><strong>S</strong></u>equential <u><strong>R</strong></u>ecommendation (DGLSR). Specifically, it utilizes the Gaussian distribution (i.e., mean and covariance embeddings) to represent nodes in the user–item bipartite graph, modeling the node uncertainty while preserving the graph structure. Subsequently, we use a graph convolutional network to update user and item node distribution embeddings, and then introduce a personalization distribution embedding fusion operation to integrate them, which generates the final sequence representation. Furthermore, we design a temporal Wasserstein self-attention mechanism. This mechanism utilizes the Wasserstein distance to measure the distributional differences between any two items in the sequence while enhancing the model’s sensitivity to temporal dynamics, thereby improving the accuracy of the next item prediction. Experiments involving four real-world datasets reveal that the DGLSR we developed exceeds the performance of SOTA methods on benchmark metrics.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 5","pages":"Article 104119"},"PeriodicalIF":7.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800676","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