Nowadays, not only the number of multimedia resources available is increasing exponentially, but also the crowd-sourced feedback volunteered by viewers generates huge volumes of ratings, likes, shares and posts/reviews. Since the data size involved surpasses human filtering and searching capabilities, there is the need to create and maintain the profiles of viewers and resources to develop recommendation systems to match viewers with resources. In this paper, we propose a personalised viewer profiling technique which creates individual viewer models dynamically. This technique is based on a novel incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations.
Nowadays, urban computing has gained a lot of interest in regards to guiding the evolution of cities into intelligent environments. These environments can be appropriated for interactions between individuals that may ultimately affect and modify their behavior. These changes require new approaches that allow us to better understand how urban computing systems should be modeled. In this work, we present UrbanContext -a new model for designing urban computing platforms that applies the theory of roles to manage the individual's context in urban environments. The theory of roles helps us understand the individual's behavior within a social environment, allowing us to model urban computing systems capable of adapting to an individual's states and needs.In order to optimize social interaction and offer secure services, UrbanContext collects data in urban atmospheres and classifies behavior according to the individual's change of roles. Likewise, UrbanContext serves as a generic model to provide interoperability as well as facilitate design, implementation, and expansion of urban computing systems. Keywords Urban Computing, Context Modeling, Pervasive Computing, Ubiquitous SpacesComputer Science • 15 (1) 2014 http://dx
Various social situations entail a collective risk. A well-known example is climate change, wherein the risk of a future environmental disaster clashes with the immediate economic interest of developed and developing countries. The collective-risk game operationalizes this kind of situations. The decision process of the participants is determined by how good they are in evaluating the probability of future risk as well as their ability to anticipate the actions of the opponents. Anticipatory behavior contrasts with the reactive theories often used to analyze social dilemmas. Our initial work can already show that anticipative agents are a better model to human behavior than reactive ones. All the agents we studied used a recurrent neural network, however, only the ones that used it to predict future outcomes (anticipative agents) were able to account for changes in the context of games, a behavior also observed in experiments with humans. This extended abstract aims to explain how we wish to investigate anticipation within the context of the collective-risk game and the relevance these results may have for the field of hybrid socio-technical systems.
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