Abstract

Abstract
By carefully recommending selected items to users, recommender systems ought to increase profit from product sales. To achieve this, recommendations need to be relevant, novel and diverse. Many approaches to this problem exist, each with its own advantages and shortcomings. This paper proposes a novel way to combine model, memory and content-based approaches in a cascade-hybrid system, where each approach refines the previous one, sequentially. It is also proposed a straight-forward way to easily incorporate time-awareness into rating matrices. This approach focuses on being intuitive, flexible, robust, auditable and avoid heavy performance costs, as opposed to black-box fashion approaches. Evaluation metrics such as Novelty Score are also for-malized and computed, in conjunction with Catalog Coverage and mean recommendation price to better capture the recommender's performance.

Abstract
Covid-19 posed several challenges to all organisations in general and to social solidarity cooperatives in particular. However, the challenges faced by these cooperatives have unique features arising from their special characteristics compared to other types of cooperatives. Therefore it is vital to study these challenges and the impacts of covid-19. This study has as main goal to understand those challenges and their impact. An exploratory study was undertaken by applying 11 interviews to 11 social solidarity cooperatives. The cooperatives were chosen to be heterogeneous among the existent cooperatives in Portugal. This study corresponds to the first phase of a project that is still underway. This article presents the main results of the content analysis of the data collected from the interviews. Data show cooperatives could promptly adapt and continue their mission under pressure from the pandemic despite the first difficulties encountered in a new and unknown situation, showing a capacity to adapt and serve their members. However, these members were also submitted to several increasing and new challenges. The adaptations were possible due to legal changes in the work organisation law, from layoff to telework, government support involving financial programs, VAT, and other tax relaxation, as well as due to human resources reorganisation and the cooperatives' staff positive attitude towards the difficulties (both leaders and general workers). Differences between the social solidarity cooperatives under study concerning digital technologies showed that those already having some infrastructure had minor adapting difficulties.

Abstract
The objective of coalition formation is to partition the agent set that gives the highest utility to the system. Over the past three decades, the process of coalition formation has been applied to various real-life applications where agents need to form efficient groups to accomplish a task. This article presents a study of the state-of-the-art approaches on the applications of coalition formation. In particular, it surveys the algorithmic approaches for optimizing the system's welfare. The algorithms are then analyzed based on a framework that consists of two dimensions: (i) the features of the problem environment, which gives an overview of the complexity level of the environment, and (ii) the features of the problem solver, which gives an overview of the solution quality. Our study analyses the approaches in terms of the framework mentioned above, justifies the use of the approaches in a particular problem setting, presents guidance to choose the right algorithmic approach for a problem at hand, and classifies the state-of-the-art approaches according to their basic working principles. This article also presents possible future directions of work to the research community. This study shows that theoretical models need more research before they can be deployed in the real world.

Abstract
Football is one of the most popular sports in the world, so the perception of the game and the prediction of results is of general interest to fans, coaches, media and gamblers. Although predicting football results is a very complex task, the football betting business has grown over time. The unpredictability of football results and the growing betting business justify the development of prediction models to support gamblers. In this article, we develop machine learning methods that take multiple statistics of previous matches and attributes of players from both teams as inputs to predict the outcome of football matches. Several prediction models were tested, with the experimental results showing encouraging performance in terms of the profit margin of football bets. © 2022 Elsevier B.V.. All rights reserved.

Abstract
The advance of technology in the automotive industry brought several new functionalities providing more efficiency and safety. This, however, has one important concern: the development has become more complex. AMALTHEA is a framework for automotive system design and development in a model-based development fashion. It includes several features, including testing, software design, simulation and traceability. This paper presents ongoing work to integrate GPU tracing in the AMALTHEA standard format for tracing execution events, thus enabling platform heterogeneity to be supported in the tracing model. © 2022, Ada-Europe. All rights reserved.