A Hybrid Multi-strategy Recommender System Using Linked Open Data

In this paper, we discuss the development of a hybrid multi-strategy book recommendation system using Linked Open Data. Our approach builds on training individual base recommenders and using global popularity scores as generic recommenders. The results of the individual recommenders are combined using stacking regression and rank aggregation. We show that this approach delivers very good results in different recommendation settings and also allows for incorporating diversity of recommendations

Large-scale Multi-label Text Classification - Revisiting Neural Networks

Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we investigate limitations of BP-MLL, a neural network (NN) architecture that aims at minimizing pairwise ranking error. Instead, we propose to use a comparably simple NN approach with recently proposed learning techniques for large-scale multi-label text classification tasks. In particular, we show that BP-MLL's ranking loss minimization can be efficiently and effectively replaced with the commonly used cross entropy error function, and demonstrate that several advances in neural network training that have been developed in the realm of deep learning can be effectively employed in this setting. Our experimental results show that simple NN models equipped with advanced techniques such as rectified linear units, dropout, and AdaGrad perform as well as or even outperform state-of-the-art approaches on six large-scale textual datasets with diverse characteristics.Comment: 16 pages, 4 figures, submitted to ECML 201

PLAY: A Profiled Linear Weighting Scheme for Understanding the Influence of Input Variables on the Output of a Deep Artificial Neural Network

Recently, deep artificial neural networks (DANNs) have been successfully applied to various pattern recognition tasks with high industrial impact. Their results are so convincing that neural nets are already tested in heavily regulated fields like medicine or finance. However, these autonomous systems are often deployed without evaluating the reasoning behind their decisions. Thus, recent research has shifted towards methods that increase the interpretability of DANNs. The goal of this paper is to explain the influence of input variables on the decision of a DANN. More precisely, we aim at improving the linear weighting scheme for the contribution of input variables (LICON), a previously introduced method which estimates the contributions of inputs in a local neighborhood, by combining it with the gobal sensitivity approach(GSA), which uses sampling to examine multiple values of an input. This allows the local influence estimation of LICON to be assessed in relation to estimates obtained from sampled input values. The effectiveness of the proposed approach is assessed via a comparative study of the involved explanation methods. Despite the computational complexity, which has to be dealt with in the future, it is shown that the proposed approach generates reasonable estimates for input contributions