Abstract Crowdsourced data streams are popular and extremely valuable in several domains, namely in tourism. Tourism crowdsourcing platforms rely on past tourist and business inputs to provide tailored recommendations to current users in real time. The continuous, open, dynamic and non‐curated nature of the crowd‐originated data demands specific stream mining techniques to support online profiling, recommendation, change detection and adaptation, explanation and evaluation. The sought techniques must, not only, continuously improve and adapt profiles and models; but must also be transparent, overcome biases, prioritize preferences, master huge data volumes and all in real time. This article surveys the state‐of‐art of adaptive and explainable stream recommendation, extends the taxonomy of explainable recommendations from the offline to the stream‐based scenario, and identifies future research opportunities.

Abstract Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to manipulation. This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The profiling is built using creator-, content- and context-based features using Natural Language Processing techniques. The explainable classification mechanism displays in a dashboard the features selected for classification and the prediction confidence. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80% accuracy and macro F-measure. This proposal is the first to jointly provide data stream processing, profiling, classification and explainability. Ultimately, the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.

Abstract Telecommunication operators compete not only for new clients, but, above all, to maintain current ones. The modelling and prediction of the top‐up behaviour of prepaid mobile subscribers allows operators to anticipate customer intentions and implement measures to strengthen customer relationship. This research explores a data set from a Portuguese operator, comprising 30 months of top‐up events, to predict the top‐up monthly frequency and average value of prepaid subscribers using offline and online multi‐target regression algorithms. The offline techniques adopt a monthly sliding window, whereas the online techniques use an event sliding window. Experiments were performed to determine the most promising set of features, analyse the accuracy of the offline and online regressors and the impact of sliding window dimension. The results show that online regression outperforms the offline counterparts. The best accuracy was achieved with adaptive model rules and a sliding window of 500,000 events (approximately 5 months). Finally, the predicted top‐up monthly frequency and average value of each subscriber were converted to individual date and value intervals, which can be used by the operator to identify early signs of subscriber disengagement and immediately take pre‐emptive measures.

This paper provides an overview of the development of a Sail Car within the European Project Semester (EPS), the international multidisciplinary engineering capstone programme offered by the Instituto Superior de Engenharia do Porto (ISEP). The main goal of EPS@ISEP is to offer a project-based educational experience to develop teamwork, communication, interpersonal and problem-solving skills in an international and multidisciplinary set up. The Sail Car team consisted of six Erasmus students, who participated in EPS@ISEP during the spring of 2019. The objective of the project was to design and develop a wind-powered, easy to drive land sailing vehicle. First, the team researched existing commercial solutions and considered the marketing, ethics and sustainability dimensions of the project. Next, based on these studies, specified the full set of requirements, designed the Sailo solution and procured the components and materials required to build a real size proof-of-concept prototype. Finally, the team assembled and tested successfully the prototype. At the end of the semester, the team considered EPS@ISEP a mind-opening opportunity.

Based on historical user information, collaborative filters predict for a given user the classification of unknown items, typically using a single criterion. However, a crowd typically rates tourism resources using multi-criteria, i.e., each user provides multiple ratings per item. In order to apply standard collaborative filtering, it is necessary to have a unique classification per user and item. This unique classification can be based on a single rating – single criterion (SC) profiling – or on the multiple ratings available – multicriteria (MC) profiling. Exploring both SC and MC profiling, this work proposes: (ı) the selection of the most representative crowd-sourced rating; and (ıı) the combination of the different user ratings per item, using the average of the non-null ratings or the personalised weighted average based on the user rating profile. Having employed matrix factorisation to predict unknown ratings, we argue that the personalised combination of multi-criteria item ratings improves the tourist profile and, consequently, the quality of the collaborative predictions. Thus, this paper contributes to a novel approach for guest profiling based on multi-criteria hotel ratings and to the prediction of hotel guest ratings based on the Alternating Least Squares algorithm. Our experiments with crowd-sourced Expedia and TripAdvisor data show that the proposed method improves the accuracy of the hotel rating predictions.

As a recommendation technique based on historical user information, collaborative filtering typically predicts the classification of items using a single criterion for a given user. However, many application domains can benefit from the analysis of multiple criteria, e.g. tourists usually rate attractions (hotels, attractions, restaurants, etc.) using multiple criteria. In this paper, we argue that the personalised combination of multi-criteria data together with the creation and application of trust models should not only refine the tourist profile, but also improve the quality of the collaborative recommendations. The main contributions of this work are: (1) a novel profiling approach which takes advantage of the multi-criteria crowdsourced data and builds pairwise trust models and (2) the k-NN prediction of user ratings using trust-based neighbour selection. Significant experimental work has been performed using crowdsourced datasets from the Expedia and TripAdvisor platforms.

The goal of the work presented in this paper is to provide mobile platforms within our campus with a GPS based data service capable of supporting precise outdoor navigation. This can be achieved by providing campus-wide access to real time Differential GPS (DGPS) data. As a result, we designed and implemented a three-tier distributed system that provides Internet data links between remote DGPS sources and the campus and a campus-wide DGPS data dissemination service. The Internet data link service is a two-tier client/server where the server-side is connected to the DGPS station and the client-side is located at the campus. The campus-wide DGPS data provider disseminates the DGPS data received at the campus via the campus Intranet and via a wireless data link. The wireless broadcast is intended for portable receivers equipped with a DGPS wireless interface and the Intranet link is provided for receivers with a DGPS serial interface. The application is expected to provide adequate support for accurate outdoor campus navigation tasks.