With the rapid growth of e-commerce platforms and user behavioral data, product recommendation systems play an increasingly important role in improving user experience, click-through rates, and long-term commercial value. However, traditional recommendation methods often suffer from data sparsity, cold-start problems, limited interpretability, and insufficient modeling of semantic relationships among users and products. To address these challenges, this study proposes an e-commerce product recommendation framework integrating knowledge graphs, with the Taobao recommendation system as the main application scenario. Overall, this study provides an effective and scalable solution for intelligent e-commerce product recommendation systems.
Urban brand visual systems shape city image and user experience across physical and digital touchpoints. However, existing methods are often static and weak in connecting user feedback with actionable visual optimization. To address this issue, this study proposes UXMap-BrandOpt, a user experience map-based dynamic optimization framework. Specifically, it constructs a dynamic user experience map, fuses visual, textual, emotional, spatial, and brand-perception features, and learns a requirement–visual element mapping to recommend optimization actions. A multi-objective decision module balances user satisfaction, brand recognition, cultural fit, visual consistency, and optimization cost. Experiments on public datasets and the Chengdu-UXBrand Dataset show that the proposed method outperforms eight baselines, achieving F1_pain=0.891, S_BR=0.842, S_VC=0.821, Acc_act=0.813, and a utility gain of 0.236. The Chengdu case demonstrates its ability to identify weak touchpoints and provide interpretable visual optimization strategies.
As the new energy vehicle (NEV) industry is increasingly characterized by personalized customization and green manufacturing, conventional centralized production scheduling approaches have become less effective in coping with dynamic disturbances and the coordination requirements of distributed manufacturing resources. To address this issue, this study develops a five-layer collaborative scheduling decision framework consisting of the physical, perception, negotiation, learning, and optimization layers, thereby operationalizing a closed-loop logic of “perception–negotiation–learning–optimization.” Specifically, the perception layer acquires real-time production-state information through a digital twin environment. These findings demonstrate the functional completeness and operational feasibility of the proposed framework for dynamic scheduling in distributed manufacturing environments and provide both theoretical support and practical guidance for intelligent scheduling system design in smart factories.
This paper proposes MCAD-Net, a multi-context collaborative decision framework for marine emergency management. The framework integrates context representation learning, risk-aware modeling, and stakeholder-aware decision fusion to support coordinated multi-stakeholder decisions under uncertain maritime conditions. By jointly encoding environmental, operational, and resource states, MCAD-Net dynamically estimates emergency risk levels and generates coordinated response strategies. Experiments on multiple marine datasets show that MCAD-Net outperforms optimization-based and reinforcement learning baselines, achieving a decision utility of 0.812 compared with 0.753 for the strongest baseline. These results demonstrate its effectiveness in improving coordination efficiency and decision reliability in marine emergency management.
Accurate shelf monitoring and category preference estimation are critical for modern retail management, yet existing pipelines largely focus on improving detection accuracy while neglecting calibration, structural alignment, and downstream decision relevance. This limitation becomes acute when deploying heterogeneous detectors such as Faster R-CNN, SSD, YOLO, or transformer-based models in real-world shelf environments characterized by occlusion, crowding, and promotional fluctuations. These results demonstrate that beyond boosting detection accuracy, aligning detectors with planogram constraints and uncertainty-aware aggregation directly enhances retail decision quality. This study highlights a paradigm shift from detector-centric benchmarking to decision-centric evaluation, offering both theoretical insights and practical tools for robust retail shelf management.
