Information Processing & Management最新文献

{"title":"Hierarchical multi-label text classification of tourism resources using a label-aware dual graph attention network","authors":"Quan Cheng,&nbsp;Wenwan Shi","doi":"10.1016/j.ipm.2024.103952","DOIUrl":"10.1016/j.ipm.2024.103952","url":null,"abstract":"<div><div>In the era of big data, classifying online tourism resource information can facilitate the matching of user needs with tourism resources and enhance the efficiency of tourism resource integration. However, most research in this field has concentrated on a simple classification problem with a single level of single labelling. In this paper, a Hierarchical Label-Aware Tourism-Informed Dual Graph Attention Network (HLT-DGAT) is proposed for the complex multi-level and multi-label classification presented by online textual information about Chinese tourism resources. This model integrates domain knowledge into a pre-trained language model and employs attention mechanisms to transform the text representation into the label-based representation. Subsequently, the model utilizes dual Graph Attention Network (GAT), with one component capturing vertical information and the other capturing horizontal information within the label hierarchy. The model's performance is validated on two commonly used public datasets as well as on a manually curated Chinese tourism resource dataset, which consists of online textual overviews of Chinese tourism resources above 3A level. Experimental results indicate that HLT-DGAT demonstrates superiority in threshold-based and area-under-curve evaluation metrics. Specifically, the <span><math><mrow><mrow><mtext>AU</mtext><mo>(</mo></mrow><mover><mrow><mtext>PRC</mtext></mrow><mo>‾</mo></mover><mrow><mo>)</mo></mrow></mrow></math></span> reaches 64.5 % on the Chinese tourism resource dataset with enforced leaf nodes, which is 3 % higher than the optimal corresponding metric of the baseline model. Furthermore, ablation studies show that (1) integrating domain knowledge, (2) combining local information, (3) considering label dependencies within the same level of label hierarchy, and (4) merging dynamic reconstruction can enhance overall model performance.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587301","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}

{"title":"ME3A: A Multimodal Entity Entailment framework for multimodal Entity Alignment","authors":"Yu Zhao,&nbsp;Ying Zhang,&nbsp;Xuhui Sui,&nbsp;Xiangrui Cai","doi":"10.1016/j.ipm.2024.103951","DOIUrl":"10.1016/j.ipm.2024.103951","url":null,"abstract":"<div><div>Current methods for multimodal entity alignment (MEA) primarily rely on entity representation learning, which undermines entity alignment performance because of cross-KG interaction deficiency and multimodal heterogeneity. In this paper, we propose a <strong>M</strong>ultimodal <strong>E</strong>ntity <strong>E</strong>ntailment framework of multimodal <strong>E</strong>ntity <strong>A</strong>lignment task, <strong>ME³A</strong>, and recast the MEA task as an entailment problem about entities in the two KGs. This way, the cross-KG modality information directly interacts with each other in the unified textual space. Specifically, we construct the multimodal information in the unified textual space as textual sequences: for relational and attribute modalities, we combine the neighbors and attribute values of entities as sentences; for visual modality, we map the entity image as trainable prefixes and insert them into sequences. Then, we input the concatenated sequences of two entities into the pre-trained language model (PLM) as an entailment reasoner to capture the unified fine-grained correlation pattern of the multimodal tokens between entities. Two types of entity aligners are proposed to model the bi-directional entailment probability as the entity similarity. Extensive experiments conducted on nine MEA datasets with various modality combination settings demonstrate that our ME<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>A effectively incorporates multimodal information and surpasses the performance of the state-of-the-art MEA methods by 16.5% at most.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587300","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}

{"title":"Impact of economic and socio-political risk factors on sovereign credit ratings","authors":"Abhinav Goel,&nbsp;Archana Singh","doi":"10.1016/j.ipm.2024.103943","DOIUrl":"10.1016/j.ipm.2024.103943","url":null,"abstract":"<div><div>Sovereign Credit Ratings (SCRs) help international investors price the risk of lending to sovereigns or entities domiciled within that sovereign, thereby impacting cost and availability of capital flows into an economy. The international credit rating agencies (CRAs - Moody's, S&amp;P and Fitch) consider both quantitative (economic) and qualitative (socio-political) factors while determining the SCR of a country. However, research in the field of SCR has focussed largely on quantitative factors giving lesser importance to qualitative factors. The present work analyses the linkage of banking sector risks and SCR, the bias in rating process towards high-income nations, and the impact of both quantitative and qualitative factors to provide a more holistic picture of the determinants of SCR.</div><div>To attain these objectives, the present work develops two datasets covering 55 countries and compiles the data for 10 years (2011–2020) in terms of SCR obtained from Moody's and Fitch, and the values for various quantitative and qualitative factors. The dataset comprises of 18,700 data points obtained from 32 independent variables; 17 are quantitative and 15 qualitative. Some qualitative factors are also introduced which were not used earlier in SCR literature The data has been collated from World Bank, International Monetary Fund, United Nations etc. Correlation analysis has been performed on these two datasets followed by the application of Extra Tree Classifier for predicting SCR. Thorough result analysis indicates that qualitative factors, individually and as a group, are more important in determining SCR than quantitative factors. The results also indicate the presence of bias towards high-income nations and moderate importance of banking parameters in determination of SCR. Further, the use of Extra Tree Classifier gives a prediction accuracy of 97 % - 98 % for dataset 1 and dataset 2, respectively. Comparative analysis with existing work proves the efficacy of the present work.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587302","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}

{"title":"CKEMI: Concept knowledge enhanced metaphor identification framework","authors":"Dian Wang ,&nbsp;Yang Li ,&nbsp;Suge Wang ,&nbsp;Xin Chen ,&nbsp;Jian Liao ,&nbsp;Deyu Li ,&nbsp;Xiaoli Li","doi":"10.1016/j.ipm.2024.103946","DOIUrl":"10.1016/j.ipm.2024.103946","url":null,"abstract":"<div><div>Metaphor is pervasive in our life, there is roughly one metaphor every three sentences on average in our daily conversations. Previous metaphor identification researches in NLP have rarely focused on similarity between concepts from different domains. In this paper, we propose a Concept Knowledge Enhanced Metaphor Identification Framework (CKEMI) to model similarity between concepts from different domains. First, we construct the descriptive concept word set and the inter-word relation concept word set by selecting knowledge from the ConceptNet knowledge base. Then, we devise two hierarchical relation concept graph networks to refine inter-word relation concept knowledge. Next, we design the concept consistency mapping function to constrain the representation of inter-word relation concept and learn similarity information between concepts. Finally, we construct the target domain semantic scene by integrating the representation of inter-word relation concept knowledge for metaphor identification. Specifically, the F1 score of CKEMI is superior to the state-of-the-art (SOTA) methods, achieving improvements of over 0.5%, 1.0%, and 1.2% on the VUA-18(10k), VUA-20(16k), and MOH-X(0.6k) datasets, respectively.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577976","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}

{"title":"Membership inference attacks via spatial projection-based relative information loss in MLaaS","authors":"Zehua Ding ,&nbsp;Youliang Tian ,&nbsp;Guorong Wang ,&nbsp;Jinbo Xiong ,&nbsp;Jinchuan Tang ,&nbsp;Jianfeng Ma","doi":"10.1016/j.ipm.2024.103947","DOIUrl":"10.1016/j.ipm.2024.103947","url":null,"abstract":"<div><div>Machine Learning as a Service (MLaaS) has significantly advanced data-driven decision-making and the development of intelligent applications. However, the privacy risks posed by membership inference attacks (MIAs) remain a critical concern. MIAs are primarily classified into score-based and perturbation-based attacks. The former relies on shadow data and models, which are difficult to obtain in practical applications, while the latter depends solely on perturbation distance, resulting in insufficient identification performance. To this end, we propose a Spatial Projection-based Relative Information Loss (SPRIL) MIA to ascertain the sample membership by flexibly controlling the size of perturbations in the noise space and integrating relative information loss. Firstly, we analyze the alterations in predicted probability distributions induced by adversarial perturbations and leverage these changes as pivotal features for membership identification. Secondly, we introduce a spatial projection technique that flexibly modulates the perturbation amplitude to accentuate the difference in probability distributions between member and non-member data. Thirdly, this quantifies the distribution difference by calculating relative information loss based on KL divergence to identify membership. SPRIL provides a solid method to assess the potential risks of DNN models in MLaaS and demonstrates its efficacy and precision in black-box and white-box settings. Finally, experimental results demonstrate the effectiveness of SPRIL across various datasets and model architectures. Notably, on the CIFAR-100 dataset, SPRIL achieves the highest attack accuracy and AUC, reaching 99.27% and 99.73%, respectively.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577975","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}

{"title":"Higher-order structure based node importance evaluation in directed networks","authors":"Meng Li ,&nbsp;Zhigang Wang ,&nbsp;An Zeng ,&nbsp;Zengru Di","doi":"10.1016/j.ipm.2024.103948","DOIUrl":"10.1016/j.ipm.2024.103948","url":null,"abstract":"<div><div>Evaluating the significance of objects with possible relevant information is a crucial topic in information science. Due to the fact that objects related to each other can often be described using complex networks, this topic also forms a fundamental theme in network science. Most traditional methods for characterizing the importance of nodes in complex networks only utilize the binary relationships between node pairs, neglecting the influence brought by higher-order structures. Considering the specific interaction modes between local nodes in the network, this paper associates the higher-order structural characteristics of the network with the importance of the nodes. It constructs an evaluation framework for the importance of nodes in directed networks based on higher-order structures. Experimental analysis on both artificial data and scientific citation data from the APS dataset has validated the effectiveness of the proposed algorithms. Compared with PageRank and eigenvector centrality, the proposed algorithms demonstrated higher accuracy, revealing the role of higher-order structures in node importance evaluation. Finally, a robustness analysis of several algorithms indicated that the proposed algorithms exhibited good robustness.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142577974","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}

{"title":"Multi-view graph contrastive representation learning for bundle recommendation","authors":"Peng Zhang ,&nbsp;Zhendong Niu ,&nbsp;Ru Ma ,&nbsp;Fuzhi Zhang","doi":"10.1016/j.ipm.2024.103956","DOIUrl":"10.1016/j.ipm.2024.103956","url":null,"abstract":"<div><div>Bundle recommendation can recommend a collection of associated items that can be consumed together to a user rather than recommending these items separately, making it extremely suitable for some scenarios such as product bundle recommendation and game bundle recommendation. Recent bundle recommendation approaches consider auxiliary data to mitigate sparse user-bundle interactions. However, these approaches obtain the node embeddings directly from the established user-bundle graph and do not explicitly exploit the relationships between users (bundles) when constructing recommendation models. Moreover, bundle recommendation approaches based on graph contrastive learning usually construct contrastive views by randomly discarding nodes (edges) in the graph, while discarding some essential nodes or edges will destroy the structure of the original graph, thereby deteriorating the quality of the learned node embeddings. Aiming at these limitations, we propose a bundle recommendation approach based on multi-view graph contrastive representation learning. First, we present a multi-view modeling method to model the relations between entities as several views from different perspectives. These views serve as inputs of graph neural networks for graph representation learning and provide contrastive views for the contrastive learning tasks. Second, we propose a novel framework for bundle recommendation. This framework obtains the user (bundle) embeddings from different views by performing multi-view graph representation learning and enhances the learned user and bundle embeddings through a two-level contrastive learning strategy. On this basis, the enhanced user (bundle) embeddings are fused for prediction. Finally, we design a joint optimization objective to optimize the model parameters, combining the prediction loss that supports multiple negative samples and the contrastive losses. Experiments on the Netease and Youshu datasets reveal that our approach outperforms the state-of-the-art (SOTA) baselines. Furthermore, the average improvements of Recall@K and NDCG@K of our approach over the SOTA baselines are approximately 3.38% and 2.80% on Netease and 3.94% and 4.84% on Youshu.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573158","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}

{"title":"Exploring the dynamics of group-based internet rumors propagation: A novel model from the perspective of random hypergraphs","authors":"Yang Xia ,&nbsp;Haijun Jiang ,&nbsp;Shuzhen Yu","doi":"10.1016/j.ipm.2024.103941","DOIUrl":"10.1016/j.ipm.2024.103941","url":null,"abstract":"<div><div>Group interactions have become an important way of online communication today. In this paper, a novel random Hyper-ISDR rumor model is proposed, which uses random hypergraphs to describe the group relationship more accurately. A key innovation of our model is the introduction of hyperpath and path indicators into the group propagation characterization for the first time, explaining the multiple path selectivity present in group propagation. Then, the theoretical conditions for the disappearance and persistence of Internet rumors are obtained by applying stochastic stability theory. This paper finds three interesting results: (1) the propagation threshold on hypergraphs is more sensitive to parameter changes than on traditional graphs; (2) the multiple selectivity of the group propagation path is a critical catalyst for swift rumor diffusion; (3) educating spreaders to become refuters rather than removers is more effective in controlling rumors. Moreover, compared with the graph-based ISDR model and the Hyper-SIR model, it shows that the hyperdegree and path indicators have a greater impact on rumor volatility. Finally, the reliability and applicability of the results are verified by numerical simulation and a real-life case study. This work not only opens up a new perspective of group rumor dynamics analysis, but also provides a superior framework for understanding and managing online information diffusion.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573156","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}

{"title":"An optimal multi-scale and multi-factor two-stage integration paradigm coupled with investor sentiment for carbon price prediction","authors":"Jujie Wang,&nbsp;Xuecheng He","doi":"10.1016/j.ipm.2024.103953","DOIUrl":"10.1016/j.ipm.2024.103953","url":null,"abstract":"<div><div>The accurate prediction of carbon emission trading prices is of great significance for the effective allocation of carbon resources, achieving energy conservation, emission reduction, and green development. However, it is difficult to fully extract the fluctuation information of carbon price, and external factors also have complex impacts on it, so it is a challenge to accurately predict carbon price. Therefore, this study proposes an optimal multi-scale and multi-factor two-stage integration paradigm coupled with investor sentiment for carbon price prediction. Firstly, an adaptive periodic variational mode decomposition (APVMD) method is proposed to capture feature subsequences with different fluctuation information from a periodic perspective in carbon prices. Then a comprehensive impact factor library is constructed to assist in prediction, including unstructured data on investor sentiment and structured data. Through the enhanced light gradient boosting machine (ELightGBM) algorithm, the optimal driving factors for each feature subsequence are fully screened, and the dimensionality of the data is reduced based on their nonlinear relationship. Considering the selection of hyperparameters and the contribution of different feature subsequences, an optimized two-stage integrated prediction is designed to achieve high-precision point prediction. On this basis, uncertainty analysis is used to obtain reasonable interval prediction results. Through comparative analysis, this model is better than other comparative models in terms of predictive ability and stability.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573157","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}

{"title":"Sequential recommendation by reprogramming pretrained transformer","authors":"Min Tang ,&nbsp;Shujie Cui ,&nbsp;Zhe Jin ,&nbsp;Shiuan-ni Liang ,&nbsp;Chenliang Li ,&nbsp;Lixin Zou","doi":"10.1016/j.ipm.2024.103938","DOIUrl":"10.1016/j.ipm.2024.103938","url":null,"abstract":"<div><div>Inspired by the success of Pre-trained language models (PLMs), numerous sequential recommenders attempted to replicate its achievements by employing PLMs’ efficient architectures for building large models and using self-supervised learning for broadening training data. Despite their success, there is curiosity about developing a large-scale sequential recommender system since existing methods either build models within a single dataset or utilize text as an intermediary for alignment across different datasets. However, due to the sparsity of user–item interactions, unalignment between different datasets, and lack of global information in the sequential recommendation, directly pre-training a large foundation model may not be feasible.</div><div>Towards this end, we propose the <span>RecPPT</span> that firstly employs the GPT-2 to model historical sequence by training the input item embedding and the output layer from scratch, which avoids training a large model on the sparse user–item interactions. Additionally, to alleviate the burden of unalignment, the <span>RecPPT</span> is equipped with a reprogramming module to reprogram the target embedding to existing well-trained proto-embeddings. Furthermore, <span>RecPPT</span> integrates global information into sequences by initializing the item embedding using an SVD-based initializer. Extensive experiments over four datasets demonstrated the <span>RecPPT</span> achieved an average improvement of 6.5% on NDCG@5, 6.2% on NDCG@10, 6.1% on Recall@5, and 5.4% on Recall@10 compared to the baselines. Particularly in few-shot scenarios, the significant improvements in NDCG@10 confirm the superiority of the proposed method.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573227","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}