Information Processing and Management: an International Journal

Empathetic response generation endeavours to perceive the interlocutor’s emotional and cognitive states in the dialogue and express proper responses. Previous studies detect the interlocutor’s states by understanding the immediate context of the ...

• We introduce situations and explore their explicit and implicit associations.
• We propose a bidirectional filtering encoder to captures the explicit associations.
• We propose a reasoning knowledge-based hypergraph neural network.

The proliferation of rumors on social networks poses a serious threat to cybersecurity, justice and public trust, increasing the urgent need for rumor detection. Existing detection methods typically treat all rumors as a single homogeneous ...

As complex multi-omics data in the medical field tend to be multi-modal. Integrating these multimodal information into novel disease diagnosis models has become challenging. However, previous methods mainly focus on single omics, which cannot ...

During mobile application development, developers often use various third-party libraries to expedite the development process and enhance application functionality. Real datasets often show significant long-tailed distribution characteristics, ...

• We propose a TPL recommendation model based on homogeneous graph neural networks.
• HGNRec splits the interaction information into two homogeneous graph.
• We incorporate a statistically-based edge construction for node aggregation.

Depression detection based on text analysis has emerged as a research hotspot. Existing research indicates that patients’ personalized characteristics are the primary factor contributing to differences in reported experiences, which poses ...

• Pioneered personalized modeling in text-based depression detection.
• Personalized models narrow the gap between generic symptoms and patient experiences.
• Defined key components of personalized information and proposed a novel ...

The introduction of Internet of Vehicles (IoV) is promising in enabling vehicles to communicate with each other. However, attackers can invade the on-board operating systems and send fabricated messages, compromising the reliability of IoV. ...

• An anomaly detection scheme is proposed to quantify the impact of a traffic event.
• The proposed scheme adapts to the varied impact of different traffic events.
• A cluster quality indicator is designed to complement clustering-based ...

Top-k Trajectory Similarity Query (k-TSQ) is a fundamental operation in trajectory analysis, aiming to identify the k most similar trajectories to the queried trajectory. However, with the increasing demand for real-time data processing, static k-...

• We propose a novel three-layer pruning paradigm HPS that incorporates well-recognized constraints and the novel LB_TY.
• The “respond-first, update-later” strategy of HPS maximizes resource utilization while mitigating load pressure.

Multi-source cross-lingual transfer aims to acquire task knowledge from multiple labelled source languages and transfer it to an unlabelled target language, enabling effective performance in this target language. The existing methods mainly focus ...

• Propose a discrepancy-guided de-noising method to learn discriminative representations.
• Propose a pseudo-label supervised mutual learning method to promote mutual guidance.
• Propose a de-noising mutual learning method for multi-...

• We introduce ChatGPT paraphrased reviews as a new challenge for online fake reviews.
• We study the association/disassociation between real and paraphrased reviews.
• We identify a consistent association/disassociation pattern.
• ...

Fake reviews corrode the trust between businesses and consumers and distort the online image of a service or a product. The problem of fake review contamination is only going to worsen with the introduction of Artificial Intelligence (AI) ...

Personalization is a prominent process in today’s recommender systems (RS) that enhances user satisfaction and platform profitability. However, recent studies suggest that over-personalization may lead to polarized user preferences, which can ...

• We propose three DQN methods for controlling user polarization in recommendations.
• ICDQN is a hard-constrained method that limits the increase in user polarization.
• RP-DQN is a soft-constrained method that penalizes polarizing item ...

• Analyze 24 million scholars’ careers across 19 disciplines from 1950 to 2015.
• Establish career stage division based on dropout rates across fields.
• Compare gender differences within each stage using matching techniques.
• ...

The slower career advancement of women hampers diversity and jeopardizes female leadership, resulting in significant setbacks for the academic community. Our study constructed a more comprehensive dataset than previous studies, encompassing 24,...

Online product information provides crucial cues for consumer shopping behavior; however, the impact of consumer-side information manipulation on non-purchase behavior (e.g., shopping cart abandonment) remains unclear. Filtering, a common ...

Fake news detection involves developing techniques to identify and flag misleading or false information disseminated through media sources. Current efforts often use limited information for categorization, lacking comprehensive data integration ...

• We propose a novel Multifaceted Reasoning Network for Explainable Fake News Detection.
• We integrate multi-source and multi-granularity information, and provide multi-angle explanations for fake news classification.
• We conducted ...

Previous temporal knowledge graph (TKG) reasoning methods often focus exclusively on evolving representations. However, these methods suffer from the inadequacy of capturing the structural nuances of concurrent facts, the intricate relations in ...

• Integrating topological relation graphs and temporal fusion information.
• Time-aware relation attention mechanism captures structural dependencies.
• Designing topological patterns to generate topological relation graphs.
• ...

Continuous sign language recognition (CSLR) is essential for the social participation of deaf individuals. The structural information of sign language motion units plays a crucial role in semantic representation. However, most existing CSLR ...

Link prediction is a critical task within the realm of graph machine learning. While recent advancements mainly emphasize node representation learning, the rich information encapsulated within edges, proven advantageous in various graph-related ...

• Incorporating edge information for link prediction.
• Edge-level instead of node-level contrastive learning.
• Leveraging MLPs for edge representation.
• Edge representation can improve link prediction.

Existing personalized federated learning frameworks fail to significantly improve the personalization of user preference learning in next Point-Of-Interest (POI) recommendations, causing notable performance deficits. These frameworks do not fully ...

B-mode ultrasound (BUS) mainly reflects the tissue structural, morphological, and echo characteristics of liver tumors, and contrast-enhanced ultrasound (CEUS) offers supplementary information on the dynamic blood perfusion pattern to promote ...

• The AD model accurately depicts the propagation process in weighted hypergraph.
• The EIOA is proposed to effectively evaluate the influence spread of node set.
• Three search algorithms have significant advantages in solving the WHIM ...

Influence maximization (IM) is a key issue in network science. However, previous research on IM has previously explored binary interaction relationship in ordinary graphs, with little consideration for higher-order interaction that are more ...

• The model learns the latent stakeholders’ preferences by embedding multi-view information sources.
• A personalized preference evaluation model is developed for each stakeholder to satisfy their goals.
• The stakeholders’ preferences ...

Learning the preferences of consumers, providers, and system stakeholders is a challenging problem in the Multi-Stakeholder Recommendation System (MSRS). Existing MSRS methods lack the ability to generate equitable recommendations and investigate ...

Rank aggregation is a vital tool in facilitating decision-making processes that consider multiple criteria or attributes. While in many applications, the available ranked lists are often limited and quite partial for various reasons. This ...

• We address the problem of rank aggregation with limited information.
• We present a networked representation of ranking information.
• We employ the link prediction to mine potential ranking information.

Topic evolution has been studied extensively in the field of the science of science. This study first analyzes topic evolution pattern from topics’ semantic consistency in the semantic vector space, and explore its possible causes. Specifically, ...

• Propose a framework for predicting scientific disruption based on graph signal processing (GSP).
• The framework is unified, allowing the utilization and integration of any information for prediction.
• The framework is scalable, ...

Identifying scientific disruption is consistently recognized as challenging, and more so is to predict it. We suggest that better predictions are hindered by the inability to integrate multidimensional information and the limited scalability of ...

• We studied the relationship between organizational diversity and scientific impact of papers.
• We analyzed citation networks among 83,634,210 items of WOS literature.
• Large teams with high organizational diversity can also disrupt ...

Large teams are known to be more likely to publish highly cited papers, while small teams are known to be better at publishing highly disruptive papers. However, there is a lack of adequate theoretical understanding of the mechanisms by which ...

The serious information overload problem of MOOCs videos decreases the learning efficiency of the students and the utilization rate of the videos. There are two problems worthy of attention for the matrix factorization (MF)-based video ...

Oracle bone characters (OBCs) are ancient ideographs for divination and memorization, as well as first-hand evidence of ancient Chinese culture. The detection of OBC is the premise of advanced studies and was mainly done by authoritative experts ...

• A semi-supervised framework called OBCTeacher is proposed for OBC detection.
• A geometric-priori assignment and a heatmap polishing procedure are introduced.
• A class information re-weighting module and a contrastive anchor loss are ...

As a crucial step in promoting data sharing, data trading can stimulate the development of the data economy. However, the current data trading market primarily focuses on satisfying data owners' interests, overlooking the demands of data ...

Despite recent advances in large language models (LLMs), the best effectiveness in information extraction (IE) is still achieved by fine-tuned models, hence the need for manually annotated datasets to train them. However, collecting human ...

Table-based fact verification focuses on determining the truthfulness of statements by cross-referencing data in tables. This task is challenging due to the complex interactions inherent in table structures. To address this challenge, existing ...

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• We propose Macol, which verifies statement accuracy by integrating relevant tables.
• By fusing multi-aspect reasoning clues, Macol guides us to obtain key insights.
• Using auto-generated adversarial instances, Macol enables fine-...