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Context-Aware Recommender Systems (CARS) have emerged as a different way of providing more precise and interesting recommendations through the use of data about the context in which consumers buy goods and/or services. CARS consider not only the ratings given to items by consumers (users), but also the context attributes related to these ratings. Several algorithms and methods have been proposed in...
Differently from traditional machine learning techniques applied to data classification, high level classification considers not only the physical features of the data (distance, similarity or distribution), but also the pattern formation of the data. In this latter case, a set of complex network measures are employed because of their abilities to capture spatial, functional and topological relations...
Brazil has a multi-party political system with 30 registered parties (as of 2013). However, anyone who knows a little about politics understands that is nearly impossible to have 30 dimensions of political positions (e.g. center, left, right, center-left, etc.) with no overlap. Hence, the obvious challenge is to understand this party system and how parties group together. However there is no obvious...
Periodic routines have been traditionally identified in Social Sciences as the essential component of social organizations that are persistent in time, with the temporal continuity of such routines constituting the foundation of the preservation of the social systems both between successive generations and between extant and immigrant populations. Open multiagent systems (MAS) with persistent social...
There is no doubt that the World Wide Web has made easier the task of searching for information on the Internet. The amount of information obtained (some of them irrelevant ones) increases day after day and creates opportunities for a new breed of systems named "Recommender Systems". These systems have emerged as one successful approach to tackle the problem of information overload. Traditional...
Currently managing information overload has become a major challenge. How to manage all these data, presented in diverse formats and originating from heterogeneous sources? This paper presents a strategy to perform data fusion effectively. Our strategy deals with the problem of object identification in the context of the Command and Control of the Brazilian Defense Ministry using MIP Data Model from...
Particle Swarm Optimization (PSO) is an evolutionary heuristics-based method used for continuous function optimization. Compared to existing stochastic methods, PSO is very robust. Nevertheless, for real-world optimizations, it requires a high computational effort. In general, parallel implementations of PSO provide better performance. However, this depends heavily on the parallelization strategy...
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