Abstract
Hill climbing and simulated annealing are two fundamental search techniques integrating most artificial intelligence and machine learning courses curricula. These techniques serve as introduction to stochastic and probabilistic based metaheuristics. Simulated annealing can be considered a hill-climbing variant with a probabilistic decision. While simulated annealing is conceptually a simple algorithm, in practice it can be difficult to parameterize. In order to promote a good simulated annealing algorithm perception by students, a simulation experiment is reported here. Key implementation issues are addressed, both for minimization and maximization problems. Simulation results are presented.

Abstract
This paper discusses the motivation and the challenges for providing a systematic and transparent approach for dealing with cross-system consistency. Our high level goal is to provide a way to avoid violations of causality when multiple systems interact, while (a) avoiding the redesign of existing systems, (b) minimizing the overhead, and (c) requiring as little developer input as possible.

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
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.