Knowledge graphs (KGs) have garnered significant attention in recommender systems as auxiliary information. Most existing studies consider an item as an entity of a KG and utilize graph neural networks to learn item representations. However, two challenges exist regarding these algorithms: 1) they provide recommended results but fail to explain the reason for which they are preferred by users; 2) user vector representations are concentrated in a small area, thus resulting in similar mass recommendations. In this study, we focus on learning fine-grained user preferences (LFUP) via user-item interactions and using KGs that can capture the reason for which users interact with items. Additionally, a personalized recommendation task is achieved by optimizing the distribution of users in the vector space. User preferences are modeled by using historical interaction items pertaining to users and important relations within the KG. Subsequently, information from two views is aggregated to reduce the semantic differences between them. Finally, user preferences are personalized by maximizing the spatial distance between various user representations via contrastive learning. Experiments on public datasets prove that LFUP significantly benefits user-preference modeling and personalized recommendations.

Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property losses. Real-world emergency material scheduling (EMS) problems are typically large-scale and possess complex constraints. An evolutionary algorithm (EA) is one of the effective methods for solving EMS problems. However, the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality constraints. To handle the above challenges, we apply the idea of a variable reduction strategy (VRS) to an EMS problem, which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS problems. Firstly, we define an emergency material allocation and route scheduling model, and a variable neighborhood search and NSGA-II hybrid algorithm (VNS-NSGAII) is designed to solve the model. Secondly, we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality constraints. Furthermore, we integrate VRS with VNS-NSGAII to solve the reduced EMS model. To prove the effectiveness of VRS on VNS-NSAGII, we construct two test cases, where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5∙12 Wenchuan earthquake emergency material support situation. Experimental results show that VRS can improve the performance of the standard VNS-NSGAII, enabling better optimization efficiency and a higher-quality solution.

Over the recent years, we have been witnessing spectacular achievements of Artificial Intelligence (AI) and Machine Learning (ML), in particular. We have seen highly visible accomplishments encountered in natural language processing and computer vision impacting numerous areas of human endeavours. Being driven inherently by the technologically advanced learning and architectural developments, ML constructs are highly impactful coming with far reaching consequences; just to mention autonomous vehicles, health care imaging, decision-making processes in critical areas, among others. The quality of ML architectures and credibility of generated results are inherently implied by the nature, quality, and amount of available data. The credibility of ML models and confidence quantified their results are also of paramount concern to any critical application. In this study, we advocate that the credibility (confidence) of results produced by ML constructs is inherently expressed in the form of information granules. Several development scenarios are carefully revisited including those involving constructs in statistics (confidence and prediction intervals), probability (Gaussian process models), and granular parameters (fuzzy sets and interval techniques). We augment the commonly encountered and challenging category of applications of ML referred to as federated learning where the aspect of quality of the model and its results calls for a thorough assessment.