Personalised Dynamic Viewer Profiling for Streamed Data

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Veloso, B (Author) Other; Malheiro, B (Author) Other; Burguillo, JC (Author) Other; Foss, JD (Author) Other; João Gama (Author) FEP

Conference proceedings International

Title: Advances in Intelligent Systems and Computing

Pages: 501-510

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-00N-R7Y

DOI: 10.1007/978-3-319-77712-2_47

Abstract (EN): 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. © Springer International Publishing AG, part of Springer Nature 2018.

Language: English

Type (Professor's evaluation): Scientific