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Recommender systems are currently being applied in many different domains. This paper focuses on their application in tourism. A comprehensive and thorough search of the smart e-Tourism recommenders reported in the Artificial Intelligence journals and conferences since 2008 has been made. The paper provides a detailed and up-to-date survey of the field, considering the different kinds of interfaces, the diversity of recommendation algorithms, the functionalities offered by these systems and their use of Artificial Intelligence techniques. The survey also provides some guidelines for the construction of tourism recommenders and outlines the most promising areas of work in the field for the next years. [ABSTRACT FROM AUTHOR]

Clustering has been used in many areas. It is an unsupervised learning method which tries to find some distributions and patterns in unlabeled data sets. Although clustering algorithms have been studied for decades, none of them is all purpose. This paper presents a new clustering algorithm, Clustering based on Near Neighbor Influence (CNNI), an improved version in time cost of CNNI algorithm (ICNNI), and a variation of CNNI algorithm (VCNNI). They are inspired by the idea of near neighbors and the superposition principle of influence. In order to clearly describe the three algorithms, it lists three basic concepts (near neighbor point set, grid cell, and near neighbor grid cell set) and introduces two important concepts (near neighbor influence and a kind of similarity measure). In the simulations, four famous clustering algorithms (K-Means, FCM, AP, and DBSCAN) are used as comparative algorithms. From the simulated experiments of some artificial data sets and some real data sets, we observed that CNNI, ICNNI, and VCNNI can find those obvious clusters and get better (or similar) clustering results than (or with) K-Means, FCM, and AP for some data sets. We also observed that ICNNI is faster than CNNI with the same clustering results, CNNI and ICNNI are faster than AP with better or similar clustering quality, CNNI needs less space than VCNNI and DBSCAN, and VCNNI gets similar clustering results with DBSCAN. Especially, CNNI, ICNNI, and VCNNI can easily find some noises or isolates. At last, it gives several solid and insightful future research suggestions. [ABSTRACT FROM AUTHOR]

Cellular networks are important for the success of modern communication systems, which support billions of mobile users and devices. Powered by artificial intelligence techniques, cellular networks are becoming increasingly smarter, and cellular traffic prediction is an important basis for realizing various applications that have originated from this trend. In this survey, we review the relevant studies on cellular traffic prediction and classify the prediction problems as the temporal and spatiotemporal prediction problems. The prediction models with artificial intelligence are categorized into statistical, machine learning, and deep learning models and then compared. Various applications based on cellular traffic prediction are summarized along with their current progress. The potential research directions are pointed out for future research. To the best of our knowledge, this paper is the first comprehensive survey on cellular traffic prediction. • Artificial intelligence for cellular traffic prediction is reviewed comprehensively. • Cellular traffic prediction problems, models, and evaluation metrics are classified. • The potential applications and directions for future research are pointed out. [ABSTRACT FROM AUTHOR]

• CASTLE aims to combine local and global features to explain any AI system predictions. • Model's global knowledge, as rules, supports the local explanation generation process. • CASTLE estimates how clusters' representative points in uence predictions. • It supports any explanation type (e.g. rule-based, feature importance, counterfactual). With Artificial Intelligence becoming part of a rapidly increasing number of industrial applications, more and more requirements about their transparency and trustworthiness are being demanded to AI systems, especially in military, medical and financial domains, where decisions have a huge impact on lives. In this paper, we propose a novel model-agnostic Explainable AI (XAI) technique, named Cluster-aided Space Transformation for Local Explanation (CASTLE) , able to provide rule-based explanations based on both the local and global model's workings, i.e. its detailed "knowledge" in the neighborhood of the target instance and its general knowledge on the training dataset, respectively. The framework has been evaluated on six datasets in terms of temporal efficiency, cluster quality and model significance. Eventually, we asked 36 users to evaluate the explainability of the framework, getting as result an increase of interpretability of 6 % with respect to another state-of-the-art technique, named Anchors. [ABSTRACT FROM AUTHOR]