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.
KeywordsOn-line viewer profiling Data stream mining Personalisation
This research was carried out in the framework of the project TEC4Growth – RL SMILES –Smart, mobile, Intelligent and Large Scale Sensing and analytics NORTE-01-0145-FEDER-000020 which is financed by the north Portugal regional operational program (NORTE 2020), under the Portugal 2020 partnership agreement, and through the European regional development fund.
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