In recent years, the interest in recommendation systems (RSs) has dramatically increased, as they have become main components of all online stores. The aims of an RS can be multifaceted, related not only to the increase in sales or the convenience of the customer, but may include the promotion of alternative environmentally friendly products or to strengthen policies and campaigns. In addition to accurate suggestions, important aspects of contemporary RSs are therefore to align with the particular marketing goals of the e-shop and with the stances of the targeted audience, ensuring user acceptance, satisfaction, high impact, and achieving sustained usage by customers. The current review focuses on RS related to retail shopping, highlighting recent research efforts towards enhanced e-shops and more efficient sustainable digital marketing and personalized promotion. The reported research was categorized by main approach, key methods, and specialized e-commerce problems addressed, while technological aspects were linked with marketing aspects. The increasing number of papers in the field showed that it has become particularly popular, following the explosive growth in e-commerce and mobile shopping. The problems addressed have expanded beyond the performance of the core algorithms to the business aspects of recommendation, considering user acceptance and impact maximization techniques. Technologies have also shifted from the improvement of classic filtering techniques to complex deep learning architectures, in order to deal with issues such as contextualization, sequence-based methods, and automatic feature extraction from unstructured data. The upcoming goals seem to be even more intelligent recommendations that more precisely adapt not only to users’ explicit needs and hidden desires but also to their personality and sensitivity for more sustainable choices.

Presents an integrated model-based processing scheme for cardiac magnetic resonance imaging (MRI), embedded in an interactive computing environment suitable for quantitative cardiac analysis, which provides a set of functions for the extraction, modeling, and visualization of cardiac shape and deformation. The methods apply 4-D processing (three spatial and one temporal) to multiphase multislice MRI acquisitions and produce a continuous 4-D model of the myocardial surface deformation. The model is used to measure diagnostically useful parameters, such as wall motion, myocardial thickening, and myocardial mass measurements. The proposed model-based shape extraction method has the advantage of integrating local information into an overall representation and produces a robust description of cardiac cavities. A learning segmentation process that incorporates a generating-shrinking neural network is combined with a spatiotemporal parametric modeling method through functional basis decomposition. A multiscale approach is adopted, which uses at each step a coarse-scale model defined at the previous step in order to constrain the boundary detection. The main advantages of the proposed methods are efficiency, lack of uncertainty about convergence, and robustness to image artifacts.

One of the goals of procurement is to establish a competitive price, while e-procurement utilises electronic commerce to identify potential sources of supply, to purchase goods and services, to exchange contractual information and to interact with suppliers. Extensive academic work has been extensively devoted to e-procurement in diverse industries. However, applying e-procurement in the healthcare sector remains unexplored. It lacks an efficient e-procurement mechanism that will enable hospitals and healthcare suppliers to electronically exchange contractual information, aided by the technologies of optimisation and business rules. The development and deployment of e-procurement requires a major effort in the coordination of complex interorganisational business process. This article presents an e-procurement optimised system (EPOS) for the healthcare marketplace, a complete methodological approach for deploying and operating such system, as piloted in public and private hospitals in three European countries (Greece, Spain and Belgium) and suppliers of healthcare items in the fourth country (Italy). The efficient e-procurement mechanism that EPOS suggests enables hospitals and pharmaceutical and medical equipment suppliers to electronically exchange contractual information.

The quality of staff is of paramount importance in all businesses. However, in maritime industry it is crucial due to the nature of the business itself. Employees' lives could be exposed to risk, let alone that huge assets are in stake, if poor decisions are made. Employing quality and professional human resources requires a personnel selection process that would evaluate the best suited personnel, dealing with the consideration of both qualitative and quantitative criteria. However, despite the significance of the maritime industry worldwide, research is needed in order to investigate the criteria upon which maritime management could rely when selecting their human resources. This paper aims at developing a multi-criteria model for personnel selection in the maritime industry, based on the Analytic Hierarchy Process (AHP), chosen for its ability to incorporate multiple subjective opinions into a solid decision model. A novel criteria ranking technique based on Correspondence Analysis (CA) was added to the process in order to select the most highly evaluated criteria through a powerful data-driven method which does not rely on a-priori scales. Data was collected by interviewing experts from 14 maritime companies in Greece. The resulting model was validated by comparing its results against previous cases of successful employment. The results indicate that criteria such as trustworthiness, responsibility, capabilities in decision making, team spirit and communication skills are among the most important traits that companies look for.