Pages
 1
 2
 document

Pocchiari, M. (author)Recommender Systems assist the user by suggesting items to be consumed based on the user's history. The topic of diversity in recommendation gained momentum in recent years as additional criterion besides recommendation accuracy, to improve user satisfaction. Accuracy and diversity in recommender systems coexist in a delicate tradeoff due to...master thesis 2020
 document

Sipko, Tomas (author)The world is generating more and more network data in many different areas (e.g., sensor networks, social networks and even text). A unique characteristic of these data is the coupling between data values and underlying irregular structure on which these values are defined. Thus, researchers developed Graph Neural Networks (GNNs) to use deep...master thesis 2020
 document

Yang, Maosheng (author)This thesis consists of two parts in both data science and signal processing over graphs. In the first part of this thesis, we aim to solve the problem of graph construction in big data scenario, which is critical for practical tasks, like collaborative filtering in recommender systems, spectral embedding or clustering in learning algorithms. We...master thesis 2020
 document

Mazzola, Gabriele (author)Timevarying network data are essential in several realworld applications, such as temperature forecasting and earthquake classification. Spatial and temporal dependencies characterize these data and, therefore, conventional machine learning tools often fail to learn these joint correlations from data. On the one hand, hybrid models to learn...master thesis 2020
 document

Iancu, Bianca (author)Network data are essential in applications such as recommender systems, social networks, and sensor networks. A unique characteristic that these data encompass is the coupling between the data values and the underlying network structure on which these data are defined. Graph Neural Networks (GNNs) have been designed as tools to extend the...master thesis 2020
Pages
 1
 2