2026
Bid Farewell to Seesaw: Towards Accurate Long-Tail Session-Based Recommendation via Dual Constraints of Hybrid Intents
AAAI 2026technical
Session-based recommendation (SBR) aims to predict anonymous users
9 accepted papers
Session-based recommendation (SBR) aims to predict anonymous users
The Plan-then-Code paradigm effectively enhances Large Language Models (LLMs) in complex code generation by decomposing reasoning into explicit, interpretable steps. However, introducing the plan and verification report substantially enlarges the context, which in turn misdirects the model’s attenti…
Spiking Federated Learning (SFL) has been widely studied with the energy efficiency of Spiking Neural Networks (SNNs). However, existing SFL methods require model homogeneity and assume all clients have sufficient computational resources, resulting in the exclusion of some resource-constrained clien
The temporal evolution patterns of surface spatial structures constitute a central concern within the field of intelligent remote sensing interpretation.However, constrained by the availability of only two temporal phases, modeling sparse spatio-temporal change processes to effectively interpret sur
Multimodal Emotion Recognition (MER) aims to accurately identify human emotional states by integrating heterogeneous modalities such as visual, auditory, and textual data. Existing approaches predominantly rely on unified emotion labels to supervise model training, often overlooking a critical chall
Large language models (LLMs) are increasingly adapted to downstream tasks in resource-constrained scenarios, making quantization-aware fine-tuning (QAF) a common practice for practical deployment. However, we find that quantized LLMs are substantially more vulnerable to safety alignment degradation …
Contemporary deep learning, characterized by the training of cumbersome neural networks on massive datasets, confronts substantial computational hurdles. To alleviate heavy data storage burdens on limited hardware resources, numerous dataset compression methods such as dataset distillation (DD) and…
The growing demand for efficient deep learning has positioned dataset distillation as a pivotal technique for compressing training dataset while preserving model performance. However, existing inner-loop optimization methods for dataset distillation typically rely on random truncation strategies, wh…
Personalized federated learning (PFL) is a new paradigm to address the statistical heterogeneity problem in federated learning. Most existing PFL methods focus on leveraging global and local information such as model interpolation or parameter decoupling. However, these methods often overlook the ge…