Personalization is a core capability across consumer technologies, streaming, shopping, wearables, and voice, yet it remains challenged by sparse interactions, fast content churn, and heterogeneous textual signals. We present RecMind, an LLM-enhanced graph recommender that treats the language model as a preference prior rather than a monolithic ranker. A frozen LLM equipped with lightweight adapters produces text-conditioned user/item embeddings from titles, attributes, and reviews; a LightGCN backbone learns collaborative embeddings from the user-item graph. We align the two views with a symmetric contrastive objective and fuse them via intra-layer gating, allowing language to dominate in cold/long-tail regimes and graph structure to stabilize rankings elsewhere. On Yelp and Amazon-Electronics, RecMind attains the best results on all eight reported metrics, with relative improvements up to +4.53\% (Recall@40) and +4.01\% (NDCG@40) over strong baselines. Ablations confirm both the necessity of cross-view alignment and the advantage of gating over late fusion and LLM-only variants.

Fuzzy Graph Attention Network (FGAT), which combines Fuzzy Rough Sets and Graph Attention Networks, has shown promise in tasks requiring robust graph-based learning. However, existing models struggle to effectively capture dependencies from multiple perspectives, limiting their ability to model complex data. To address this gap, we propose the Multi-view Fuzzy Graph Attention Network (MFGAT), a novel framework that constructs and aggregates multi-view information using a specially designed Transformation Block. This block dynamically transforms data from multiple aspects and aggregates the resulting representations via a weighted sum mechanism, enabling comprehensive multi-view modeling. The aggregated information is fed into FGAT to enhance fuzzy graph convolutions. Additionally, we introduce a simple yet effective learnable global pooling mechanism for improved graph-level understanding. Extensive experiments on graph classification tasks demonstrate that MFGAT outperforms state-of-the-art baselines, underscoring its effectiveness and versatility.

In this paper, we propose ShapTST, a framework that enables time-series transformers to efficiently generate Shapley-value-based explanations alongside predictions in a single forward pass. Shapley values are widely used to evaluate the contribution of different time-steps and features in a test sample, and are commonly generated through repeatedly inferring on each sample with different parts of information removed. Therefore, it requires expensive inference-time computations that occur at every request for model explanations. In contrast, our framework unifies the explanation and prediction in training through a novel Shapley-based pre-training design, which eliminates the undesirable test-time computation and replaces it with a single-time pre-training. Moreover, this specialized pre-training benefits the prediction performance by making the transformer model more effectively weigh different features and time-steps in the time-series, particularly improving the robustness against data noise that is common to raw time-series data. We experimentally validated our approach on eight public datasets, where our time-series model achieved competitive results in both classification and regression tasks, while providing Shapley-based explanations similar to those obtained with post-hoc computation. Our work offers an efficient and explainable solution for time-series analysis tasks in the safety-critical applications.

The urgent need to transition toward carbon neutrality has positioned smart grids as a cornerstone of sustainable energy systems. This study investigates how advanced algorithms and socio-behavioral strategies can enhance the role of smart grids in promoting low-carbon lifestyles. A multi-dimensional framework is proposed, integrating four key components: the Vickrey-Clarke-Groves (VCG) mechanism for optimizing incentive allocation, Long Short-Term Memory (LSTM) models for dynamic energy pricing, Natural Language Processing (NLP) for user behavior analysis, and Social Network Analysis (SNA) for evaluating socio-cultural impacts.The research demonstrates that the VCG mechanism ensures fairness and efficiency in resource distribution, significantly increasing user participation in energy-saving actions. LSTM-based pricing models enable precise forecasting of energy demand, encouraging consumption shifts to off-peak periods and reducing carbon emissions. NLP techniques uncover behavioral patterns from textual data, enabling personalized incentive designs that improve engagement rates. SNA reveals the diffusion dynamics of low-carbon behaviors across networks, highlighting key influencers and the depth of cultural transformation. Quantitative evaluations using metrics such as prediction accuracy, user response rates, and fairness indices validate the effectiveness of these methods. This interdisciplinary approach bridges technological advancements and societal engagement, offering a comprehensive pathway to accelerate the adoption of sustainable practices. The findings provide actionable insights for policymakers and utility companies, emphasizing the potential of smart grids as a catalyst for carbon neutrality and long-term cultural change.