Abstract
Tourism crowdsourcing platforms have a profound influence on the tourist behaviour particularly in terms of travel planning. Not only they hold the opinions shared by other tourists concerning tourism resources, but, with the help of recommendation engines, are the pillar of personalised resource recommendation. However, since prospective tourists are unaware of the trustworthiness or reputation of crowd publishers, they are in fact taking a leap of faith when then rely on the crowd wisdom. In this paper, we argue that modelling publisher Trust & Reputation improves the quality of the tourism recommendations supported by crowdsourced information. Therefore, we present a tourism recommendation system which integrates: (i) user profiling using the multi-criteria ratings; (ii) k-Nearest Neighbours (k-NN) prediction of the user ratings; (iii) Trust & Reputation modelling; and (iv) incremental model update, i.e., providing near real-time recommendations. In terms of contributions, this paper provides two different Trust & Reputation approaches: (i) general reputation employing the pairwise trust values using all users; and (ii) neighbour-based reputation employing the pairwise trust values of the common neighbours. The proposed method was experimented using crowdsourced datasets from Expedia and TripAdvisor platforms. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Nowadays tourists rely on technology for inspiration, research, booking, experiencing and sharing. Not only it provides access to endless sources of information, but has become an unbounded source of tourist-related data. In such crowd-sourced data-intensive scenario, we argue that new approaches are required to enrich current and new travelling experiences. This work, which supports the "dreaming stage", proposes the automatic recommendation of personalised destinations based on textual reviews, i.e.,a semantic content-based filter of crowd-sourced information. Our approach relies on Topic Modelling - to extract meaningful information from textual reviews - and Semantic Similarity to identify relevant recommendations. Our main contribution is the processing of crowd-sourced tourism information employing data mining techniques in order to automatically discover untapped destinations on behalf of tourists.

Abstract
This paper describes the collaborative learning environment, aligned with the United Nations Millennium Development Goals, provided by the European Project Semester (EPS). EPS is a one semester capstone project programme offered by eighteen European engineering schools as part of their student exchange programme portfolio. In this international programme, students are organized in teams, grouping individuals from diverse academic backgrounds and nationalities. The teams, after choosing a project proposal, become fully responsible for the conduction of their projects. By default, project proposals refer to open multidisciplinary real problems. The purpose of the project is to expose students to problems of a greater dimension and complexity than those faced throughout the degree programme as well as to put them in contact with the so-called real world, in opposition to the academic world. EPS provides an integrated framework for undertaking capstone projects, which is focused on multicultural and multidisciplinary teamwork, communication, problem-solving, creativity, leadership, entrepreneurship, ethical reasoning and global contextual analysis. Specifically, the design and development of sustainable systems for growing food allow students not only to reach the described objectives, but to foster sustainable development practices. As a result, we recommend the adoption of this category of projects within EPS for the benefit of engineering students and of the society as a whole.

Abstract
The scalable analysis of crowdsourced data repositories and streams has quickly become a critical experimental asset in multiple fields. It enables the systematic aggregation of otherwise disperse data sources and their efficient processing using significant amounts of computational resources. However, the considerable amount of crowdsourced social data and the numerous criteria to observe can limit analytical off-line and on-line processing due to the intrinsic computational complexity. This paper demonstrates the efficient parallelisation of profiling and recommendation algorithms using tourism crowdsourced data repositories and streams. Using the Yelp data set for restaurants, we have explored two different profiling approaches: entity-based and feature-based using ratings, comments, and location. Concerning recommendation, we use a collaborative recommendation filter employing singular value decomposition with stochastic gradient descent (SVD-SGD). To accurately compute the final recommendations, we have applied post-recommendation filters based on venue suitability, value for money, and sentiment. Additionally, we have built a social graph for enrichment. Our master-worker implementation shows super-linear scalability for 10, 20, 30, 40, 50, and 60 concurrent instances.

Abstract
Nowadays travellers can benefit from the computing capabilities, collection of on board sensors and ubiquitous Internet access provided by mobile devices. These are the three pillars of any tourist support system since they provide the power, means and data to establish the local user context, to access remote services and to provide value-added user-centred context-aware applications. However, making sense of the user context data is not straightforward, as it requires dedicated knowledge acquisition and knowledge representation solutions. Besides, the range and diversity of available data sources is huge, requiring appropriate knowledge processing techniques to provide addequated tourism services. This article presents an updated review, and a comparison of recent context-aware tourism applications (CATA), including supporting technologies; and considering four possible dimensions: knowledge acquisition, knowledge representation, knowledge processing and knowledge-based services. We propose and apply a CATA analysis framework, contemplating these four dimensions to the applications found in the literature. This survey constitutes, not only, a state of the art review on tourism mobile applications, but, also, anticipates the latest development trends in tourism-related applications.

Abstract
The widespread usage of smart devices and sensors together with the ubiquity of the Internet access is behind the exponential growth of data streams. Nowadays, there are hundreds of machine learning algorithms able to process high-speed data streams. However, these algorithms rely on human expertise to perform complex processing tasks like hyper-parameter tuning. This paper addresses the problem of data variability modelling in data streams. Specifically, we propose and evaluate a new parameter tuning algorithm called Self Parameter Tuning (SPT). SPT consists of an online adaptation of the Nelder & Mead optimisation algorithm for hyper-parameter tuning. The method explores a dynamic size sample method to evaluate the current solution, and uses the Nelder & Mead operators to update the current set of parameters. The main contribution is the adaptation of the Nelder-Mead algorithm to automatically tune regression hyper-parameters for data streams. Additionally, whenever concept drifts occur in the data stream, it re-initiates the search for new hyper-parameters. The proposed method has been evaluated on regression scenario. Experiments with well known time-evolving data streams show that the proposed SPT hyper-parameter optimisation outperforms the results of previous expert hyper-parameter tuning efforts. © 2018, Springer Nature Switzerland AG.