Information Processing & Management最新文献

{"title":"Moment matching of joint distributions for unsupervised domain adaptation","authors":"","doi":"10.1016/j.ipm.2024.103944","DOIUrl":"10.1016/j.ipm.2024.103944","url":null,"abstract":"<div><div>Unsupervised Domain Adaptation (UDA) is designed to transfer acquired knowledge from the source domain to an unlabeled target domain. In this paper, we present a comprehensive approach that seamlessly addresses both source-available and source-free UDA by matching the joint distributions across domains, independent of the availability of source data. Our methodology introduces three innovative criteria to quantitatively assess the divergences between the source and target data, as well as between the source model hypothesis and target data. The criteria decide whether the predicted labels of the target hypothesis are affected by the other knowledge of both domains in the form of a precise formula, thereby enabling targeted supervision in UDA. We evaluate the effectiveness through 37 image and text classification tasks across four different datasets, comparing their performance against the state-of-the-art models. Experiments demonstrate that the proposed approaches obtain superior accuracies for most of the tasks, especially for the source-free setting, which still exceeds HOMDA 0.6% on Office and DRDA 1.5% on Office-Home, even without direct access to source data.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553583","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":"Quantity forecast of mobile subscribers with Time-Dilated Attention","authors":"","doi":"10.1016/j.ipm.2024.103940","DOIUrl":"10.1016/j.ipm.2024.103940","url":null,"abstract":"<div><div>The quantity forecast of mobile subscribers requires accurate and reliable results for obtaining insights into user trends and facilitating effective business management. Due to the complexity inherent in mobile subscriber data, influenced by subscriber tendencies and device popularity, capturing its underlying regularities poses a challenge. In this research, a novel Time-Dilated Attention (TDA) model is proposed, complemented by a feature extraction method characterized by high interpretability and distinguishability. Its efficacy and implications are explored on a real-world mobile subscriber dataset. TDA facilitates the acquisition of more informative representations, while our feature extraction method enhances the ability to discern dissimilar samples, thereby improving the stability of mobile subscriber trend analysis. The approach is validated on three additional datasets to assess its robustness. Experimental findings on the target mobile subscriber dataset demonstrate that the proposed approach achieves reductions in MAE, RMSE, and Theil’s U by 1.45%, 5.28%, and 5.12%, respectively, compared to the strongest baseline methods. Additionally, it attains the second-best performance in terms of MedAE. Furthermore, this model consistently ranks within the top two positions for nine out of twelve metrics on the additional datasets, underscoring its generalizability.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553582","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":"Spatial and temporal twin-guided pattern recurrent graph network for implementing reasoning of spatiotemporal knowledge graph","authors":"","doi":"10.1016/j.ipm.2024.103942","DOIUrl":"10.1016/j.ipm.2024.103942","url":null,"abstract":"<div><div>The extrapolation of knowledge graphs (KGs) has been the subject of numerous studies. However, real world data often has complex spatial attributes, which makes reasoning on spatiotemporal knowledge graphs (STKGs) challenging. In response, we propose a model that captures both temporal and spatial patterns to address the challenge of predicting future facts in STKGs. The proposed spatial and temporal twin-guided pattern recurrent graph network (STTP-RGN) utilizes temporal and spatial sequences to identify cyclic and repetitive patterns in data. It performs spatiotemporal-twin encoding and temporal and spatial sequence encoding respectively, and inputs the encoded three results into three corresponding decoders to determine the evolution of entity and predicate representations in time and space. We used the YAGO10K, Wikidata40K, Opensky18K and DY-NB21K for tests on entity and predicate prediction. On YAGO10K, the model's entity prediction performance outperforms the best temporal extrapolation model RETIA by 20 %. The predicate and entity predictions on Wikidata40K have improved by 3 % and 20 %, respectively. Results for entity prediction on Opensky18K have increased by 30 %, while results for predicate prediction have improved by 1 %. The experimental results demonstrate that the model fills the gap in knowledge extrapolation on STKG.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553495","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":"A comprehensive survey on social engineering attacks, countermeasures, case study, and research challenges","authors":"","doi":"10.1016/j.ipm.2024.103928","DOIUrl":"10.1016/j.ipm.2024.103928","url":null,"abstract":"<div><div>Social engineering attacks are inevitable and imperil the integrity, security, and confidentiality of the information used on social media platforms. Prominent technologies, such as blockchain, artificial intelligence (AI), and proactive access controls, were adopted in the literature to confront the social engineering attacks on social media. Nevertheless, a comprehensive survey on this topic is notably absent from the current body of research. Inspired by that, we propose an exhaustive survey comprising an in-depth analysis of 10 distinct social engineering attacks with their real-time scenarios. Furthermore, a detailed solution taxonomy is presented, offering valuable insights (e.g., objective, methodology, and results) to tackle social engineering attacks effectively. Based on the solution taxonomy, we propose an AI and blockchain-based malicious uniform resource locator (URL) detection framework (as a case study) to confront social engineering attacks on the Meta platform. For that, a standard dataset is utilized, which comprises 12 different datasets containing 3980870 malicious and non-malicious URLs. To classify URLs, a binary classification problem is formulated and solved by using different AI classifiers, such as Naive Bayes (NB), decision tree (DT), support vector machine (SVM), and boosted tree (BT). The non-malicious URLs are forwarded to the blockchain network to ensure secure storage, strengthening the effectiveness of the malicious URL detection framework. The proposed framework is evaluated with baseline approaches, wherein the NB achieves noteworthy training accuracy, i.e., 76.87% and training time of (8.23 (s)). Additionally, interplanetary file system (IPFS)-based blockchain achieves a remarkable response time, i.e., (0.245 (ms)) compared to the conventional blockchain technology. We also used execution cost and smart contract vulnerability assessment using Slither to showcase the outperformance of blockchain technology. Lastly, we shed light on the open issues and research challenges of social engineering attacks where research gaps still exist and require further investigation.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142553496","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":"Supporting group cruise decisions with online collective wisdom: An integrated approach combining review helpfulness analysis and consensus in social networks","authors":"","doi":"10.1016/j.ipm.2024.103936","DOIUrl":"10.1016/j.ipm.2024.103936","url":null,"abstract":"<div><div>Online cruise reviews provide valuable insights for group cruise evaluations, but the vast quantity and varied quality of reviews pose significant challenges. Further complications arise from the intricate social network structures and divergent preferences among decision-makers (DMs), impeding consensus on cruise evaluations. This paper proposes a novel two-stage methodology to address these issues. In the first stage, an inherent helpfulness level–personalized helpfulness level (IHL–PHL) model is devised to evaluate review helpfulness, considering not only inherent review quality but also personalized relevance to the specific DMs’ contexts. Leveraging deep learning techniques like Sentence-BERT and neural networks, the IHL–PHL model identifies high-quality, highly relevant reviews tailored as decision support data for DMs with limited cruise familiarity. The second stage facilitates consensus among DMs within overlapping social trust networks. A binary trust propagation method is developed to optimize trust propagation across overlapping communities by strategically selecting key bridging nodes. Building upon this, a constrained maximum consensus model is proposed to maximize group agreement while limiting preference adjustments based on trust-constrained willingness, thereby preventing inefficient iterations. The proposed model is verified with a dataset of 7481 reviews for four cruise alternatives. Finally, some comparisons, theoretical and practical implications are provided. Overall, this paper offers a comprehensive methodology for real-world group cruise evaluation, using online reviews from platforms like CruiseCritic as a form of collective wisdom to support decision-making.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536245","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":"Category-guided multi-interest collaborative metric learning with representation uniformity constraints","authors":"","doi":"10.1016/j.ipm.2024.103937","DOIUrl":"10.1016/j.ipm.2024.103937","url":null,"abstract":"<div><div>Multi-interest collaborative metric learning has recently emerged as an effective approach to modeling the multifaceted interests of a user in recommender systems. However, two issues remain unexplored. (1) There is no explicit guidance for the matching of an item against multiple interest vectors of a user. (2) The desired property of item representations with respect to their categories is overlooked, resulting in that different categories of items are mixed up in the latent space. To overcome these issues, we devise a Category-guided Multi-interest Collaborative Metric Learning model (CMCML) with representation uniformity constraints. CMCML is designed as a novel category-guided Mixture-of-Experts (MoE) architecture, where the gating network leverages the item category to guide the matching of an item against multiple interest vectors of a user, encouraging items with the same category to approach the same interest vector. In addition, we design a user multi-interest uniformity loss and a category-aware item uniformity loss: The former aims to avoid representation degeneration by enlarging the difference among multiple interest vectors of the same user; the latter is tailored to push different categories of items apart in the latent space. Quantitative experiments on Ciao, Epinions and TaFeng demonstrate that our CMCML improves the value of NDCG@20 by 12.41%, 10.89% and 10.39% respectively, compared to other state-of-the-art collaborative metric learning methods. Further qualitative analyses reveal that our CMCML yields a better representation space where items from distinct categories are arranged in different regions with high density.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536067","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":"Graph similarity learning for cross-level interactions","authors":"","doi":"10.1016/j.ipm.2024.103932","DOIUrl":"10.1016/j.ipm.2024.103932","url":null,"abstract":"<div><div>Graph similarity computation is crucial in fields such as bioinformatics, e.g., identifying compounds with similar biological activities by comparing molecular structural similarities. Traditional methods such as graph edit distance (GED) and maximal common subgraphs suffer from high computational complexity and sensitivity to noise, which limit their practical applications. Existing deep learning methods make it difficult to extract graph features, which affects computational accuracy comprehensively. To address these problems, we propose a new method, CLSim, which improves performance by enhancing feature extraction and improving graph similarity computation. Using the attention mechanism, CLSim first aligns graph pair features to the shared space and aggregates node features into global embeddings. The directionality of the embedding vectors is considered when extracting graph-level features to handle more complex data. In addition, we develop cross-layer feature extraction techniques that combine node-level information with graph-level embeddings to capture detailed node-graph interaction details. Experimental results on three datasets show that CLSim has excellent generalization capabilities and achieves lower error rates compared to the GED approach and the graph neural network baseline. In the worst case, its time complexity remains quadratic. Example query results further validate the effectiveness of the model, providing a more efficient and accurate solutions for graph similarity tasks.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536065","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":"Heuristic personality recognition based on fusing multiple conversations and utterance-level affection","authors":"","doi":"10.1016/j.ipm.2024.103931","DOIUrl":"10.1016/j.ipm.2024.103931","url":null,"abstract":"<div><div><strong>P</strong>ersonality <strong>R</strong>ecognition in <strong>C</strong>onversations (<strong>PRC</strong>) is a task of significant interest and practical value. Existing studies on the PRC task utilize conversation inadequately and neglect affective information. Considering the way of information processing of these studies is not yet close enough to the concept of personality, we propose the SAH-GCN model for the PRC task in this study. This model initially processes the original conversation input to extract the central speaker feature. Leveraging Contrastive Learning, it continuously adjusts the embedding of each utterance by incorporating affective information to cope with the semantic similarity. Subsequently, the model employs Graph Convolutional Networks to simulate the conversation dynamics, ensuring comprehensive interaction between the central speaker feature and other relevant features. Lastly, it heuristically fuses central speaker features from multiple conversations involving the same speaker into one comprehensive feature, facilitating personality recognition. We conduct experiments using the recently released CPED dataset, which is the personality dataset encompassing affection labels and conversation details. Our results demonstrate that SAH-GCN achieves superior accuracy (+1.88%) compared to prior works on the PRC task. Further analysis verifies the efficacy of our scheme that fuses multiple conversations and incorporates affective information for personality recognition.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536066","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":"LacGCL: Lightweight message masking with linear attention and cross-view interaction graph contrastive learning for recommendation","authors":"","doi":"10.1016/j.ipm.2024.103930","DOIUrl":"10.1016/j.ipm.2024.103930","url":null,"abstract":"<div><div>Graph contrastive learning (GCL) has recently attracted significant attention in the field of recommender systems. However, many GCL methods aim to enhance recommendation accuracy by employing dense matrix operations and frequent manipulation of graph structures to generate contrast views, leading to substantial computational resource consumption. While simpler GCL methods have lower computational costs, they fail to fully exploit collaborative filtering information, leading to reduced accuracy. On the other hand, more complex adaptive methods achieve higher accuracy but at the expense of significantly greater computational cost. Consequently, there exists a considerable gap in accuracy between these lightweight models and the more complex GCL methods focused on high accuracy.</div><div>To address this issue and achieve high predictive accuracy while maintaining low computational cost, we propose a novel method that incorporates attention-wise graph reconstruction with message masking and cross-view interaction for contrastive learning. The attention-wise graph reconstruction with message masking preserves the structural and semantic information of the graph while mitigating the overfitting problem. Linear attention ensures that the algorithm’s complexity remains low. Furthermore, the cross-view interaction is capable of capturing more high-quality latent features. Our results, validated on four datasets, demonstrate that the proposed method maintains a lightweight computational cost and significantly outperforms the baseline methods in recommendation accuracy.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536247","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":"QAIE: LLM-based Quantity Augmentation and Information Enhancement for few-shot Aspect-Based Sentiment Analysis","authors":"","doi":"10.1016/j.ipm.2024.103917","DOIUrl":"10.1016/j.ipm.2024.103917","url":null,"abstract":"<div><div>Aspect-based Sentiment Analysis (ABSA) aims to extract fine-grained sentiment information from online reviews. Few-shot ABSA faces challenges with limited labeled data and recent generative models have outperformed traditional classification models. Existing methods use Question Answering (QA) templates with Text-to-Text Transfer Transformer (T5) to extract sentiment elements, introducing a generative sentiment analysis paradigm. However, these models often fail to fully grasp ABSA rules, generating non-standard or incorrect outputs. This issue also arises with large language models (LLMs) due to insufficient labeled data for tuning and learning. Additionally, ABSA datasets often include many short, uninformative reviews, complicating sentiment element extraction in few-shot scenarios. This paper addresses two major challenges in few-shot ABSA: (1) <em>How to let the generative model well understand the ABSA rules under few-shot scenarios</em>. (2) <em>How to enhance the review text with richer information</em>. We propose a <strong>Q</strong>uantity <strong>A</strong>ugmentation and <strong>I</strong>nformation <strong>E</strong>nhancement (<strong>QAIE</strong>) approach, leveraging LLMs to generate fluent texts and infer implicit information. First, we propose a quantity augmentation module, which leverages the large language model (LLM) to obtain sufficient labeled data for the generative model to learn the ABSA rules better. Then, we introduce an information enhancement module, which brings more informative input to the generative model by enhancing the information in the review. Comprehensive experiments on five ABSA tasks using three widely-used datasets demonstrate that our QAIE model achieves approximately 10% improvement over state-of-the-art models. Specifically, for the most challenging ASQP task, our LLM-based model is compared with the existing state-of-the-art models on datasets Rest15 and Rest16, achieving F1 gains of 9.42% and 6.45% respectively in the <span><math><mrow><mi>k</mi><mo>=</mo><mn>5</mn></mrow></math></span> few-shot setting.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536246","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}