Sequential recommendation aims to predict the next item that a user will interact with based on historical behavior sequences. In real-world scenarios, user-item interactions exhibit complex dependencies, which graph neural networks are well-suited to model by capturing high-order relationships between nodes. However, most existing graph-based sequential recommendation methods face two major challenges: 1) they often neglect positional information within sequences when constructing graphs; and 2) they suffer from noise introduced by accidental or unintended clicks. Recent advances in large language models (LLMs) offer a promising direction for mitigating these issues, due to their strong semantic understanding. However, directly leveraging LLMs may face task mismatch and excessive denoising may exacerbate the data sparsity. To this end, we propose an LLM-enhanced position-aware graph for sequential recommendation (LEPG4SR). Specifically, we design a position-aware item transition graph to model complex item relationships from a global perspective. We then utilize LLMs to extract semantic embeddings of item side information and filter out noisy data based on semantic similarity. To further combat data sparsity, we introduce a self-supervised learning strategy with a novel semantic perturbation-based data augmentation technique. Extensive experiments on three real-world datasets demonstrate that LEPG4SR can outperform the state-of-the-art sequential recommendation methods.

The ultra-dense low-earth orbit (LEO) satellite networks (ULSNs) have become an important component of next-generation (6G) wireless networks, offering large-scale coverage and high-capacity service. Unlike traditional terrestrial network backbones deployed in closed, protected environments, satellite networks are exposed to highly dynamic environments, where space environment interference can severely affect channel conditions. This paper addresses the impact of sun outages, one of the most significant spatial interference factors, on satellite communication and proposes an adaptive coding and modulation scheme that dynamically adjusts the modulation and coding schemes (MCSs) based on real-time channel conditions to enhance the performance and communication quality of ULSNs. First, we model the channel environment of satellite-terrestrial microwave and inter-satellite laser links under sun outage interferences in ULSNs, which involves sun outage occurrence prediction and their interference quantification. Subsequently, to implement the ACM scheme, we use the seasonal autoregressive integrated moving average (SARIMA) algorithm combined with the bidirectional long short-term memory (BiLSTM) algorithm for time-series prediction of the channel state. Based on this, we apply the knowledge distillation-assisted proximal policy optimization (KD-PPO) algorithm to select the appropriate MCS. Simulation results show that by calculating the sun outage duration and the resulting interference, as well as predicting the channel state, the proposed KD-PPO algorithm can minimize the bit error rate (BER) and maximize the spectrum utilization (SU) in ULSNs.

This study proposes an innovative model (i.e., SensoryT5), which integrates sensory knowledge into the T5 (Text-to-Text Transfer Transformer) framework for emotion classification tasks. By embedding sensory knowledge within the T5 model’s attention mechanism, SensoryT5 not only enhances the model’s contextual understanding but also elevates its sensitivity to the nuanced interplay between sensory information and emotional states. Experiments on four emotion classification datasets, three sarcasm classification datasets one subjectivity analysis dataset, and one opinion classification dataset (ranging from binary to 32-class tasks) demonstrate that our model outperforms state-of-the-art baseline models (including the baseline T5 model) significantly. Specifically, SensoryT5 achieves a maximal improvement of 3.0% in both the accuracy and the F1 score for emotion classification. In sarcasm classification tasks, the model surpasses the baseline models by the maximal increase of 1.2% in accuracy and 1.1% in the F1 score. Furthermore, SensoryT5 continues to demonstrate its superior performances for both subjectivity analysis and opinion classification, with increases in ACC and the F1 score by 0.6% for the subjectivity analysis task and increases in ACC by 0.4% and the F1 score by 0.6% for the opinion classification task, when compared to the second-best models. These improvements underscore the significant potential of leveraging cognitive resources to deepen NLP models’ comprehension of emotional nuances and suggest an interdisciplinary research between the areas of NLP and neuro-cognitive science. • SensoryT5: Integrates sensory knowledge into transformers, enhancing emotion analysis. • Outperforms other models in emotion, sarcasm, subjectivity, and opinion classifications on various datasets. • Offers improved word representation and interpretability with sensory knowledge. • Advances NLP by embedding neuro-cognitive data in text-classification frameworks.

Social relations are often used as auxiliary information to address data sparsity and cold-start issues in social recommendations. In the real world, social relations among users are complex and diverse. Widely used graph neural networks (GNNs) can only model pairwise node relationships and are not conducive to exploring higher-order connectivity, while hypergraph provides a natural way to model high-order relations between nodes. However, recent studies show that social recommendations still face the following challenges: 1) a majority of social recommendations ignore the impact of multifaceted social relationships on user preferences; 2) the item homogeneity is often neglected, mainly referring to items with similar static attributes have similar attractiveness when exposed to users that indicating hidden links between items; and 3) directly combining the representations learned from different independent views cannot fully exploit the potential connections between different views. To address these challenges, in this article, we propose a novel method DH-HGCN++ for multiple social recommendations. Specifically, dual homogeneity (i.e., social homogeneity and item homogeneity) is introduced to mine the impact of diverse social relations on user preferences and enrich item representations. Hypergraph convolution networks with motifs are further exploited to model the high-order relations between nodes. Finally, cross-view contrastive learning is proposed as an auxiliary task to jointly optimize the DH-HGCN++. Real-world datasets are used to validate the effectiveness of the proposed model, where we use sentiment analysis to extract comment relations and employ the k-means clustering algorithm to construct the item-item correlation graph. Experiment results demonstrate that our proposed method consistently outperforms the state-of-the-art baselines on Top-N recommendations.

Social relations are often used as auxiliary information to improve recommendations. In the real-world, social relations among users are complex and diverse. However, most existing recommendation methods assume only single social relation (i.e., exploit pairwise relations to mine user preferences), ignoring the impact of multifaceted social relations on user preferences (i.e., high order complexity of user relations). Moreover, an observing fact is that similar items always have similar attractiveness when exposed to users, indicating a potential connection among the static attributes of items. Here, we advocate modeling the dual homogeneity from social relations and item connections by hypergraph convolution networks, named DH-HGCN, to obtain high-order correlations among users and items. Specifically, we use sentiment analysis to extract comment relation and use the k-means clustering to construct item-item correlations, and we then optimize those heterogeneous graphs in a unified framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.