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.

Abstract
The IPP4B workshop assembles a group of researchers from academia and industry - BBC R&D, Ericsson and MOG Technologies - to discuss the state of the art and together envisage future directions for in-programme personalisation in broadcasting. The workshop comprises one invited keynote, two invited presentations together with a paper and discussion sessions.

Abstract

Abstract
This study addresses long-term sea level variability in Macaronesia from a holistic perspective using all available instrumental records in the region, including a dense network of GPS continuous stations, tide gauges and satellite observations. A detailed assessment of vertical movement from GPS time series underlines the influence of the complex volcano-tectonic setting of the Macaronesian islands in local uplift/subsidence. Relative sea level for the region is spatially highly variable, ranging from -1.1 to 5.1 mm yr(-1). Absolute sea level from satellite altimetry exhibits consistent trends in the Macaronesia, with a mean value of 3.0 +/- 0.5 mm yr(-1). Typically, sea level trends from tide gauge records corrected for vertical movement using the estimates from GPS time series are lower than uncorrected estimates. The agreement between satellite altimetry and tide gauge trends corrected for vertical land varies substantially from island to island. Trends derived from the combination of GPS and tide gauge observations differ by less than 1 mm yr(-1) with respect to absolute sea level trends from satellite altimetry for 56 per cent of the stations, despite the heterogeneity in length of both GPS and tide gauge series, and the influence of volcanic-tectonic processes affecting the position of some GPS stations.