Abstract
Nearest neighbour collaborative filtering (NNCF) algorithms are commonly used in multimedia recommender systems to suggest media items based on the ratings of users with similar preferences. However, the prediction accuracy of NNCF algorithms is affected by the reduced number of items - the subset of items co-rated by both users typically used to determine the similarity between pairs of users. In this paper, we propose a different approach, which substantially enhances the accuracy of the neighbour selection process - a user-based CF (UbCF) with semantic neighbour discovery (SND). Our neighbour discovery methodology, which assesses pairs of users by taking into account all the items rated at least by one of the users instead of just the set of co-rated items, semantically enriches this enlarged set of items using linked data and, finally, applies the Collinearity and Proximity Similarity metric (CPS), which combines the cosine similarity with Chebyschev distance dissimilarity metric. We tested the proposed SND against the Pearson Correlation neighbour discovery algorithm off-line, using the HetRec data set, and the results show a clear improvement in terms of accuracy and execution time for the predicted recommendations.

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Abstract
This descriptive study explores the state of CSR education and research in Portugal. It aims to depict the state of CSR in Portugal, in particular in what concerns the nature and the extent of education and research on CSR being undertaken in Portugal. The methodology used includes analysis of relevant literature and of business schools’ websites, and a survey by questionnaire among students at the business school at which the authors of this chapter teach and research. In terms of CSR practices, there is still some focus on social issues, given the state of development of the Portuguese economy. There are signs of CSR having reached a reasonable level of maturity. Although CSR education and research seem to be reasonably well developed in the leading business schools, the same is not the case with other schools. Research on CSR is concentrated in the schools in which CSR education is more developed.

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Abstract

Abstract
Sugarcane is an important product to the Brazilian economy because it is the primary ingredient of ethanol which is used as a gasoline substitute. Sugarcane is aflected by many factors which can be modelled in a Bayesian Graph. This paper describes a technique to build a Causal Bayesian Network from information in news stories. The technique: extracts causal relations from news stories, converts them into an event graph, removes irrelevant information, solves structure problems, and clusters the event graph by topic distribution. Finally, the paper describes a method for generating inferences from the graph based upon evidence in agricultural news stories. The graph is evaluated through a manual inspection and with a comparison with the EMBRAPA sugarcane taxonomy. © ACM 2016.