Abstract
This paper describes a forgetting technique for the live update of viewer profiles based on individual sliding windows, fading and incremental matrix factorization. The individual sliding window maintains, for each viewer, a queue holding the last n viewer ratings. As new viewer events occur, they are inserted in the viewer queue, by shifting and fading the queue ratings, and the viewer latent model is faded. We explored time, rating-and-position and popularity-based fading techniques, using the latter as the base fading algorithm. This approach attempts to address the problem of dynamic viewer profile updating (volatile preferences) as well as the problem of bounded processing resources (fixed size queues). The results show that our approach outperforms previous approaches, improving the quality of the predictions.

Abstract
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.

Abstract

Abstract
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 multi criteria (MC) profiling. Exploring both SC and MC profiling, this work proposes: (iota) the selection of the most representative crowd-sourced rating; and (iota iota) 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.

Abstract
This paper provides an overview of the development of a selforiented solar mirror (SOSM) project within the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP). While the main objective of the EPS@ISEP project-based educational framework is to foster teamwork, communication, interpersonal and problem solving skills in an international, multidisciplinary engineering environment, the goal of the SOSM is to track and reflect the Sun radiation onto a pre-defined area. In the spring of 2017 a group of five students chose to develop a proof-of-concept domestic SOSM called SUNO. The students undertook project supportive modules in Ethics, Sustainability, Marketing and Project Management together with project coaching meetings to assist the development of SUNO. The paper details this process, describing the initial project definition, the research of current technologies, the designing, the manufacturing and testing of the SUNO prototype, and discusses what the students gained from this learning experience. © 2017 Association for Computing Machinery.

Abstract
Crowdsourcing has become an essential source of information for tourists and the tourism industry. Every day, large volumes of data are exchanged among stakeholders in the form of searches, posts, shares, reviews or ratings. This paper presents a tourist-centred analysis of crowd-sourced hotel information collected from the Expedia platform. The analysis relies on Data Mining methodologies to predict trends and patterns which are relevant to tourists and businesses. First, we propose an approach to reduce the crowd-sourced data dimensionality, using correlation and Multiple Linear Regression to identify the single most representative rating. Finally, we use this rating to model the hotel customers and predict hotel ratings, using the Alternating Least Squares algorithm. In terms of contributions, this work proposes: (i) a new crowd-sourced hotel data set; (ii) a crowd-sourced rating analysis methodology; and (iii) a model for the prediction of personalised hotel ratings.