Ivens Portugal

TL;DR: The study concludes that Bayesian and decision tree algorithms are widely used in recommender systems because of their relative simplicity, and that requirement and design phases of recommender system development appear to offer opportunities for further research.

TL;DR: In this paper, the authors present a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies research opportunities for software engineering research, and conclude that Bayesian and decision tree algorithms are widely used in recommendation systems because of their relative simplicity and that requirement and design phases of recommender system development appear to offer opportunities for further research.

TL;DR: This literature survey identifies and describes domain-specific languages and frameworks used for Machine Learning in Big Data with the intention of assisting software engineers in making more informed choices and providing beginners with an overview of the main languages used in this domain.

TL;DR: This literature survey identifies and describes domain-specific languages and frameworks used for Machine Learning in Big Data so that software engineers can make more informed choices and beginners have an overview of the main languages used in this domain.

TL;DR: A framework to identify, process, and analyze relationships between clusters of spatial-temporal data (e.g. enter, merge, or split) is introduced, as well as a proposed clustering technique, the different approaches for distance calculation, and how the result of these operations are used in the identification of cluster relationships over space and time.