| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 573.33 KB | Adobe PDF |
Advisor(s)
Abstract(s)
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.
Description
Keywords
On-line Viewer Profiling Data Stream Mining Personalisation
Pedagogical Context
Citation
Publisher
Springer International Publishing