Link Prediction is a classic social networks analysis problem. Knowing in advance future actions in social network can help, for example, agents decision. Link Prediction techniques are based on metrics that have diff...
详细信息
ISBN:
(纸本)9781509019168
Link Prediction is a classic social networks analysis problem. Knowing in advance future actions in social network can help, for example, agents decision. Link Prediction techniques are based on metrics that have different approaches. In this paper, we model a multi-relational scientific social network to assess the impact of content extraction on topological metrics. Thus, a metric composed of topological and semantic approach is proposed to solve link prediction problem. The results were compared with those presented by Katz metric.
With the advance of mobile applications market, there is an increasing concern about the challenges when developing products that meet the many types of users and to harmonize each product with the various usage envir...
详细信息
ISBN:
(纸本)9781509019168
With the advance of mobile applications market, there is an increasing concern about the challenges when developing products that meet the many types of users and to harmonize each product with the various usage environments, thus, providing a good user experience. This situation leads to the need to develop a framework that makes applications become aware of the context and provides a self-learning of the user navigation. This article presents a theoretical framework and the TURAP operation process, a framework for Android applications that helps to tackle these challenges.
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