Ensemble of incremental multilabel classifiers for tag recommending in social networks
Szymon Chojnacki, Mieczysław A. Kłopotek · 2013
The purpose of this article is to present profits and costs of enriching state of the art real life tag recommender system with incremental learning mechanisms. We describe modifications to a system that successfully participated in Online Task of ECML/PKDD Discovery Challenge 2009. The system’s architecture follows an idea to construct hierarchical ensemble of simple classifiers, which was implemented in various ways by the systems with highest performance in the Challenge. The system is currently integrated as a web service with BibSonomy bookmarking portal and outperforms other algorithms in terms of effective latency. We focus on incremental learning techniques that improve quality of the system’s recommendations, but do not raise maintainability, efficiency or reliability issues.