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- Plug-in Diffusion Model for Sequential Recommendation112
A novel Plug-in Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items to alleviate data sparsity.
- Modeling Disease Progression via Multisource Multitask Learners: A Case Study With Alzheimer’s Disease99
A novel and unified scheme to coregularize the prior knowledge of source consistency and temporal smoothness of chronic disease progression is proposed and theoretically proves that the proposed model is a linear model.
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- Multi-Source Domain Adaptation for Visual Sentiment Classification79
A novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN), for visual sentiment classification that learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution.
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- Multimodal Conditioned Diffusion Model for Recommendation72
A novel Multimodal Conditioned Diffusion Model for Recommendation (MCDRec), which tailors DM with two technical modules to model the high-order multimodal knowledge and bridges the insurmountable gap between the multi-modal content features and the collaborative signals.
- Heterogeneous Fusion of Semantic and Collaborative Information for Visually-Aware Food Recommendation70
Experimental results from two real-world datasets show that the visual features generated by PiNet better attend to the informative image regions, yielding superior performance.
- Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data69
A cross-silo prototypical calibration method (FedCSPC), which takes additional prototype information from the clients to learn a unified feature space on the server side and develops an augmented contrastive learning method to improve the robustness of the calibration.
- Towards online and personalized daily activity recognition, habit modeling, and anomaly detection for the solitary elderly through unobtrusive sensing65
A novel framework, named the online daily habit modeling and anomaly detection (ODHMAD) model, for the real-time personalized ADL recognition, habit modeling, and anomalies detection for the solitary elderly is presented.
- Triple Sequence Learning for Cross-domain Recommendation61
A novel framework, termed triple sequence learning for cross-domain recommendation (Tri-CDR), which jointly models the source, target, and mixed behavior sequences to highlight the global and target preference and precisely model the triple correlation in CDR.
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- Self-organizing neural networks for universal learning and multimodal memory encoding40
This paper shows how a family of biologically-inspired self-organizing neural networks, known as fusion Adaptive Resonance Theory (fusion ART), may provide a viable approach to realizing the learning and memory functions.
- Learning Using Privileged Information for Food Recognition40
Experiments on two real-world datasets show that ATNet can be incorporated with any state-of-the-art CNN models to consistently improve their performance and is motivated by the paradigm of learning using privileged information (LUPI).
- Exploring False Hard Negative Sample in Cross-Domain Recommendation33
A novel model-agnostic Real Hard Negative Sampling (RealHNS) framework specially for cross- domain recommendation (CDR), which aims to discover the false and refine the real from all HNS via both general and cross-domain real hard negative sample selectors.
- Used Car Price Prediction Based on the Iterative Framework of XGBoost+LightGBM32
It is verified that the iterative framework proposed in this paper can be applied to other models and greatly improve the prediction performance of other models.
- Negative Sampling in Recommendation: A Survey and Future Directions29
This survey discusses existing user feedback, the critical role of negative sampling and the optimization objectives in RS, and thoroughly analyze challenges that consistently impede its progress, including an extensive literature review on the existing negative sampling strategies in RS.
- Cross-Training with Prototypical Distillation for improving the generalization of Federated Learning26
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- Clustering-based Curriculum Construction for Sample-Balanced Federated Learning25
A Clustering-based curriculum construction method to rank the complexity of instances, and a Federation curriculum learning algorithm (FedAC) that assigns different weights to training samples of different complexity to take full advantage of the valuable learning knowledge from a noisy and uneven-quality data is proposed.
- Cross-modal learning using privileged information for long-tailed image classification24
A cross-modal enhanced method for long-tailed image classification, referred to as CMLTNet, which improves the learning of intraclass similarity of tail-class representations by cross-modal alignment and captures the difference between the head and tail classes in semantic space by cross-modal inference.
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- Anything to Glyph: Artistic Font Synthesis via Text-to-Image Diffusion Model23
This paper proposes a novel method for generating creative artistic fonts using a pre-trained text-to-image diffusion model that can preserve the object’s original shape while constraining its position and introduces a novel heatmap-based weak position constraint method.
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- Prompt Learning with Cross-Modal Feature Alignment for Visual Domain Adaptation19
Aiming at the problem of knowledge forgetting during ”fine-tuning”, an image classification algorithm (PMHANet) integrating a pre-trained large-scale model and heterogeneous feature alignment is proposed, providing a cross-modal knowledge transfer paradigm for multimodal pre-training of large models.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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