Abstract
TourismShare is a context-aware recommendation platform that allows tourists to share private locations and videos and obtain recommendations regarding potential Points of Interest (POI), including complementary articles and videos. The user experience is enhanced with the addition audio immersion during video playback and automatic recommendation features. The developed system consists of a distributed application comprising a front-end client module (Android application), which provides the user interface and consumes directly external support services, and the back-end server module, which includes the central database and recommendation service. The communication between the client and server modules is implemented by a dedicated application level protocol. The recommendations, which are based on the user context (user position, date and current time, past ratings and user activity level), are provide on request or automatically, whenever POI of great relevance to the user are found. The recommended POI are presented on a map, showing the timetable together with complementary articles and videos. The audio immersion at video playback time takes into account the weather conditions of the video recording and the user activity level.

Abstract
This paper presents the development process of a sustainable solution to grow aromatic plants in small houses. The solution is called The GreenHouse and is meant for people who live in small houses or city apartments and want fresh home grown aromatic plants, but have neither the time nor the space to grow them. The solution is intended to be sustainable and appropriate for people concerned with eating healthy, fresh food. The project was developed by a team of five students enrolled in the European Project Semester (EPS) at the Instituto Superior de Engenharia do Porto (ISEP) during the spring of 2017. EPS@ISEP is a project-based learning framework which aims to foster personal, teamwork and multidisciplinary problem-solving skills in engineering, business and product design students. Research and discussions within the team were done to develop the product. The existing solutions for growing fresh food in industrial and domestic applications as well as marketing, sustainability and ethical topics were researched and discussed. This way it was possible to define the requirements of The GreenHouse. The GreenHouse is semi-automatic and requires little interaction from the customer. It has two covers, a winter cover and a summer cover, to be changed depending on the season and weather. Solar energy and rainwater are used to enable the growth of aromatic plants, making this a sustainable system. The support is adaptable and made to fit different support sizes so it can be hanged on balconies or windows. © 2017 Association for Computing Machinery.

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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.

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