1,951 Result(s)
within
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Database Management
Information Systems Applications (incl. Internet)
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Article
HyperMatch: long-form text matching via hypergraph convolutional networks
Semantic text matching plays a vital role in diverse domains, such as information retrieval, question answering, and recommendation. However, longer texts present challenges, including noise, long-range depend...
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Hyper-relational knowledge graph neural network for next POI recommendation
With the advancement of mobile technology, Point of Interest (POI) recommendation systems in Location-based Social Networks (LBSN) have brought numerous benefits to both users and companies. Many existing work...
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Graph neural architecture search with heterogeneous message-passing mechanisms
In recent years, neural network search has been utilized in designing effective heterogeneous graph neural networks (HGNN) and has achieved remarkable performance beyond manually designed networks. Generally, ...
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GTHP: a novel graph transformer Hawkes process for spatiotemporal event prediction
The event sequences with spatiotemporal characteristics have been rapidly produced in various domains, such as earthquakes in seismology, electronic medical records in healthcare, and transactions in the finan...
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Open AccessWhen large language models meet personalization: perspectives of challenges and opportunities
The advent of large language models marks a revolutionary breakthrough in artificial intelligence. With the unprecedented scale of training and model parameters, the capability of large language models has bee...
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Exploring highly concise and accurate text matching model with tiny weights
In this paper, we propose a simple and general lightweight approach named AL-RE2 for text matching models, and conduct experiments on three well-studied benchmark datasets across tasks of natural language inf...
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Probabilistic graph model and neural network perspective of click models for web search
Click behavior is a typical user behavior in the web search. How to capture and model users’ click behavior has always been a common research topic. However, there are few review studies on this topic. In this...
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PopGR: Popularity reweighting for debiasing in group recommendation
Like common recommender systems, group recommendation usually suffers from popularity bias where popular items are more likely to be suggested and exposed to users over long-tailed ones. The skewed data distri...
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A new neighbourhood-based diffusion algorithm for personalized recommendation
Object ratings in recommendation algorithms are used to represent the extent to which a user likes an object. Most existing recommender systems use these ratings to recommend the top-K objects to a target user...
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A supervised contrastive learning-based model for image emotion classification
Images play a vital role in social media platforms, which can more vividly reflect people’s inner emotions and preferences, so visual sentiment analysis has become an important research topic. In this paper, w...
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Progressive spatial–temporal transfer model for unsupervised person re-identification
Over the past decade, a more widespread area of computer vision research has been person re-identification (P-Reid). This technology is applied in fields such as pedestrian tracking, security, and video survei...
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Ontology-based text convolution neural network (TextCNN) for prediction of construction accidents
The construction industry suffers from workplace accidents, including injuries and fatalities, which represent a significant economic and social burden for employers, workers, and society as a whole. The exist...
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Article
Improving stock trend prediction with pretrain multi-granularity denoising contrastive learning
Stock trend prediction (STP) aims to predict price fluctuation, which is critical in financial trading. The existing STP approaches only use market data with the same granularity (e.g., as daily market data). ...
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Adaptive graph contrastive learning with joint optimization of data augmentation and graph encoder
Graph contrastive learning (GCL) has been successfully used to solve the problem of the huge cost of graph data annotation, such as labor cost, time cost, and professional knowledge cost. Recent works have foc...
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DAABNet: depth-wise asymmetric attention bottleneck for real-time semantic segmentation
With the increasing demand for the real-world applications such as autonomous driving and video surveillance, lightweight semantic segmentation methods achieving good trade-offs in terms of parameter size, spe...
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Open AccessPrivacy-preserving data publishing: an information-driven distributed genetic algorithm
The privacy-preserving data publishing (PPDP) problem has gained substantial attention from research communities, industries, and governments due to the increasing requirements for data publishing and concerns...
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Text-assisted attention-based cross-modal hashing
As one of the hottest research topics in multimedia information retrieval, cross-modal hashing has drawn widespread attention in the past decades. How to minimize the semantic gap of heterogeneous data and acc...
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Graph neural architecture prediction
Graph neural networks (GNNs) have shown their superiority in the modeling of graph data. Recently, increasing attention has been paid to automatic graph neural architecture search, aiming to overcome the short...
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A novel image denoising algorithm combining attention mechanism and residual UNet network
Images are easily polluted by noise in the process of acquisition and transmission, which will affect people's understanding and utilization of knowledge and information in images. Therefore, image denoising, ...
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Community-aware graph embedding via multi-level attribute integration
Graph embedding has been extensively studied in the literature and is widely used in various applications such as drug discovery, social network analysis, and natural language processing. However, existing app...
