Using the user’s past activity across different domains, the cross-domain recommendation (CDR) predicts the items that users are likely to click. Most recent studies on CDR model user interests at the item level. However because items in other ...

• A cross-domain recommendation method with intention disentanglement and contrast is proposed.
• Intention disentanglement module helps comprehensively represent the user’s purpose for interacting with an item.
• Intention contrastive ...

Cross-platform User Alignment (UA) aims to identify accounts belonging to the same individual across multiple social network platforms. This study seeks to enhance the performance of UA tasks while reducing the required sample data. Previous ...

Travel recommendation aims to infer travel intentions of users by analyzing their historical behaviors on Online Travel Agencies (OTAs). However, crucial keywords in clicked travel product titles, such as destination and itinerary duration, ...

• Keywords in title are treated as an additional supervision signal to improve recommendation quality.
• Long-term and short-term user preferences are learned from historical and current sessions.
• Two types of contrastive learning help ...

Long prompt leads to huge hardware costs when using transformer-based Large Language Models (LLMs). Unfortunately, many tasks, such as summarization, inevitably introduce long documents, and the wide application of in-context learning easily ...

• We are the first to use the frozen LLM itself to compress over-limit prompts.
• We achieve a balance among training cost, inference efficiency, and response quality.
• Our method is more general and cost-efficient than existing ...

As the volume of submitted papers continues to rise, ensuring a fair and accurate assignment of manuscripts to reviewers has become increasingly critical for academic conference organizers. Given the paper-reviewer similarity scores, this study ...

• Individual relative fairness in the Reviewer Assignment Problem is introduced.
• A novel formulation of the Reviewer Assignment Problem as a Coloring Problem.
• Necessary conditions for feasible, fair and balanced assignments are ...

• 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 ...

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 ...

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, ...

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 ...

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 ...

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 ...

• 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 ...

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 ...

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 ...

• We utilize large-scale scholarly datasets to quantify the duration of peer review process for interdisciplinary research.
• Contrary to the widely observed notion that interdisciplinary research often has long-term impact, we find that ...

With the growing prominence of interdisciplinary research and heightened concerns surrounding extended prepublication timelines, we still lack of understanding regarding the interplay between interdisciplinary level and the duration of the peer ...

The task of event relation extraction (ERE) aims to organize multiple events and their relations as a directed graph. However, existing ERE methods exhibit two limitations: (1) Events in a document are typically expressed with merely a trigger ...

• We propose a graph propagation model for event relation extraction.
• We investigate rich event structures for this task with a novel joint model.
• A novel triadic contrastive training method to enable high-order interactions.
• It ...

Large language models (LLMs) exhibit impressive capabilities across diverse tasks in natural language processing. Nevertheless, challenges arise such as large model parameter size and limited model accessibility through APIs such as ChatGPT and ...

• Finetuning a GIS-specific language model that can answer geospatial questions.
• Providing benchmark datasets for training and evaluating GIS language models.
• A framework for curating datasets and training a specialized large ...

• A novel multi-domain fake news detection model is proposed for domain generalization in fake news detection.
• Entities in news articles are key to learning both domain-invariant and domain-specific news representations.
• We introduce ...

Fake news has become a significant concern in recent times, particularly during the COVID-19 pandemic, as spreading false information can pose significant public health risks. Although many models have been suggested to detect fake news, they are ...