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WorldCist'18 - 6th World Conference on Information Systems and Technologies

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Personalised Dynamic Viewer Profiling for Streamed Data

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.

Bruno Veloso

Benedita Malheiro

Juan Carlos Burguillo
Universidad de Vigo

Jeremy Foss
Birmingham City University
United Kingdom


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