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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. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
Forgetting is often considered a malfunction of intelligent agents; however, in a changing world forgetting has an essential advantage. It provides means of adaptation to changes by removing effects of obsolete (not necessarily old) information from models. This also applies to intelligent systems, such as recommender systems, which learn users' preferences and predict future items of interest. In this work, we present unsupervised forgetting techniques that make recommender systems adapt to changes of users' preferences over time. We propose eleven techniques that select obsolete information and three algorithms that enforce the forgetting in different ways. In our evaluation on real-world datasets, we show that forgetting obsolete information significantly improves predictive power of recommender systems.

Abstract