2018
Autores
Cunha, T; Soares, C; de Carvalho, ACPLF;
Publicação
CoRR
Abstract
2018
Autores
Rivolli, A; Garcia, LPF; Soares, C; Vanschoren, J; de Carvalho, ACPLF;
Publicação
CoRR
Abstract
2018
Autores
Cunha, T; Soares, C; de Carvalho, ACPLF;
Publicação
CoRR
Abstract
2018
Autores
Shekar, AK; de Sá, CR; Ferreira, H; Soares, C;
Publicação
CoRR
Abstract
2018
Autores
Lindert, Dt; de Sá, CR; Soares, C; Knobbe, AJ;
Publicação
CoRR
Abstract
2018
Autores
Veloso, B; Malheiro, B; Burguillo, JC; Foss, JD; Gama, J;
Publicação
WorldCIST (2)
Abstract
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.
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