Abstract
Recommender Systems have become increasingly popular, propelling the emergence of several algorithms. As the number of algorithms grows, the selection of the most suitable algorithm for a new task becomes more complex. The development of new Recommender Systems would benefit from tools to support the selection of the most suitable algorithm. Metalearning has been used for similar purposes in other tasks, such as classification and regression. It learns predictive models to map characteristics of a dataset with the predictive performance obtained by a set of algorithms. For such, different types of characteristics have been proposed: statistical and/or information-theoretical, model-based and landmarkers. Recent studies argue that landmarkers are successful in selecting algorithms for different tasks. We propose a set of landmarkers for a Metalearning approach to the selection of Collaborative Filtering algorithms. The performance is compared with a state of the art systematic metafeatures approach using statistical and/or information-theoretical metafeatures. The results show that the metalevel accuracy performance using landmarkers is not statistically significantly better than the metafeatures obtained with a more traditional approach. Furthermore, the baselevel results obtained with the algorithms recommended using landmarkers are worse than the ones obtained with the other metafeatures. In summary, our results show that, contrary to the results obtained in other tasks, these landmarkers are not necessarily the best metafeatures for algorithm selection in Collaborative Filtering.

Abstract
In recent years, the demand from online customers has become a major problem, the variety of choice and the need for them to feel special triggered the desire to customize the products they want to buy. This high customer demand for customized products has been one of the biggest obstacles that companies have encountered. To achieve an adequate response to customer demands, companies need to adopt tools that offer to customers exactly what they want, in other words, allow customers to customize the products they want to buy. This research aims to perceive if using CMS platforms and low-cost software might be a proper solution for enterprises who intend to not only sell their products online, but also want their customers to be able to perform live product customization during their purchase. © Springer International Publishing AG 2017.

Abstract
We address the problem of whether networks of mobile devices such as smart-phones or tablets can be used to perform opportunistic, best-effort, parallel computations. We designed and implemented P3-Mobile, a parallel programming system for edge-clouds of Android devices to test the feasibility of this idea. P3-Mobile comes with a programming model that supports parallel computations over peer-to-peer overlays mapped onto mobile networks. The system performs automatic load-balancing by using the overlay to discover work. We present preliminary performance results for a parallel benchmark, using up to 16 devices, and discuss their implications towards future work. Copyright © 2017 ACM.

Abstract
Products should guarantee a sufficiently high user experience for all intended user groups. A good user experience means not only that it is sufficient to allow users to work effectively and efficiently with a product, but also that non-task-related quality aspects need to be considered. To attain various levels of user experience thus requires an efficient method to access the subjective impressions of a larger number of users toward the product. The authors show that this can be done with little effort by using a user experience questionnaire. However, such questionnaires must be adapted to the language and even to the level of language understanding of the intended target groups. Using the example of the creation of a Portuguese language version and a special version for children, the paper illustrates how the quality of the resulting adapted questionnaire can be evaluated.

Abstract
The realization of tabletop optical analogue experiments of superfluidity relies on the engineering of suitable optical media, with tailored optical properties. This work shows how quantum atomic optical systems can be used to develop highly tunable optical media, with localized control of both linear and nonlinear susceptibility. Introducing the hydrodynamic description of light, the superfluidity of light in these atomic media is investigated through GPU-enhanced numerical simulations, with the numeric observation of the superfluidic signature of suppressed scattering through a defect.

Abstract
This chapter presents the early stages of a metadata application profile (MAP) development that uses a process of reverse engineering. The context of this development is the European poetry, more specifically the poetry metrics and all dimensions that exist around this context. This community of practice has a certain number of digital repertoires that store this information and that are not interoperable. This chapter presents some steps of the definition of the MAP Domain Model. It shows how the developers having as starting point these repertoires, and by means of a reverse engineering process are modeling the functional requirements of each repertoire using the use-case modeling technique and are analyzing every database logical models to extract the conceptual model of each repertoire. The final goal is to develop a common conceptual model in order to use it as basis, together with other sources of information, for the definition of the Domain Model.