Abstract

Abstract

Abstract
Crowdsourcing has become an essential source of information for tourism stakeholders. Every day, tourists leave large volumes of feedback data in the form of posts, likes, textual reviews, and ratings in dedicated crowdsourcing platforms. This behavior makes the analysis of crowdsourced information strategic, allowing the discovery of important knowledge regarding tourists and tourism resources. This paper presents a survey on the analysis and prediction of hotel ratings from crowdsourced data, covering both off-line (batch) and on-line (stream-based) processing. Specifically, it reports multiple rating-based profiling, recommendation, and evaluation techniques. While most of the surveyed works adopt entity-based multicriteria profiling, prerecommendation filtering, and off-line processing, the latest hotel rating prediction trends include feature-based, trust and reputation modeling, postrecommendation filtering, and on-line processing. Additionally, since the volume of crowdsourced ratings tends to increase, the deployment of profiling and recommendation algorithms on high-performance computing resources should be further explored.

Abstract
Information and Communication Technologies (ICT) have revolutionised the tourism domain, providing a wide set of new services for tourists and tourism businesses. Both tourists and tourism businesses use dedicated tourism platforms to search and share information generating, constantly, new tourism crowdsourced data. This crowdsourced information has a huge influence in tourist decisions. In this context, the paper proposes a stream recommendation engine supported by crowdsourced information, adopting Stochastic Gradient Descent (SGD) matrix factorisation algorithm for rating prediction. Additionally, we explore different (i) profiling approaches (hotel-based and theme-based) using hotel multi-criteria ratings, location, value for money (VfM) and sentiment value (StV); and (ii) post-recommendation filters based on hotel location, VfM and StV. The main contribution focusses on the application of post-recommendation filters to the prediction of hotel guest ratings with both hotel and theme multi-criteria rating profiles, using crowdsourced data streams. The results show considerable accuracy and classification improvement with both hotel-based and theme-based multi-criteria profiling together with location and StV post-recommendation filtering. While the most promising results occur with the hotel-based version, the best theme-based version shows a remarkable memory conciseness when compared with its hotel-based counterpart. This makes this theme-based approach particularly appropriate for data streams. The abstract completely needs to be rewritten. It does not provide a clear view of the problem and its solutions the researchers proposed. In addition, it should cover five main elements, introduction, problem statement, methodology, contributions and results. Done.

Abstract
Wiki-based crowdsourced repositories have increasingly become an important source of information for users in multiple domains. However, as the amount of wiki-based data increases, so does the information overloading for users. Wikis, and in general crowdsourcing platforms, raise trustability questions since they do not generally store user background data, making the recommendation of pages particularly hard to rely on. In this context, this work explores scalable multi-criteria profiling using side information to model the publishers and pages of wiki-based crowdsourced platforms. Based on streams of publisher-page-review triads, we have modelled publishers and pages in terms of quality and popularity using different criteria and user-page-view events collected via a wiki platform. Our modelling approach classifies statistically, both page-review (quality) and pageview (popularity) events, attributing an appropriate rating. The quality-related information is then merged employing Multiple Linear Regression as well as a weighted average. Based on the quality and popularity, the resulting page profiles are then used to address the problem of recommending the most interesting wiki pages per destination to viewers. This paper also explores the parallelisation of profiling and recommendation algorithms using wiki-based crowdsourced distributed data repositories as data streams via incremental updating. The proposed method has been successfully evaluated using Wikivoyage, a tourism crowdsourced wiki-based repository.

Abstract
This paper summarises the joint efforts of a multinational group of six undergraduate students cooperating within the European Project Semester (EPS) conducted at the Instituto Superior de Engenharia do Porto (ISEP). The EPS@ISEP initiative, made available as a part of the Erasmus+ international students exchange programme, employs the principles of problem-based learning, facing students with—albeit downscaled—real-life scenarios and tasks they may encounter in their future professional practice. Participation in the project initiative outclasses most of the traditional courses through a wide spawn of its learning outcomes. Participants acquire not only hard skills necessary for an appropriate execution of the project, but also broaden their understanding of the approached problem through detailed scientific, management, marketing, sustainability, and ethics analysis—all in the atmosphere of multicultural and interdisciplinary collaboration. The team under consideration, based on personal preferences and predispositions, chose the topic of vertical farming and, in particular, to design a domestic indoor gardening solution, appropriate for space efficient incubation of plants. The paper portrays the process, from research, analysis, formulation of the idea to the design, development and testing of a minimum viable proof of concept prototype of the “Vereatable” solution. © 2019, Springer Nature Switzerland AG.