With the recent COVID-19 outbreak, we have assisted to the development of new epidemic models or the application of existing methodologies to predict the virus spread and to analyze how the different lock-down strategies can effectively influence the epidemic diffusion. In this paper, we propose a novel machine learning based framework able to estimate the parameters of any epidemiological model, such as contact rates and recovery rates, based on static and dynamic features of places. In particular, we model mobility data through a graph series whose spatial and temporal features are investigated by combining Graph Convolutional Neural Networks (GCNs) and Long short-term memories (LSTMs) in order to infer the parameters of SIR and SIRD models. We evaluate the proposed approach using data related to the COVID-19 dynamics in Italy and we compare the forecasts of the trained model with available data about the epidemic spread.

In recent years, we have witnessed an ever wider spread of multimedia streaming platforms (e.g., Netflix, Spotify, and Amazon). Hence, it has become more and more essential to provide such systems with advanced recommendation facilities, in order to support users in browsing these massive collections of multimedia data according to their preferences and needs. In this context, the modeling of entities and their complex relationships (e.g., users listening to topic-based songs or authors creating different releases of their lyrics) represents the key challenge to improve the recommendation and maximize the users' satisfaction. To this end, this is the first study to leverage the high representative power of hypergraph data structures in combination with modern graph machine learning techniques in the context of music recommendation. Specifically, we propose hypergraph embeddings for music recommendation (HEMR), a novel framework for song recommendation based on hypergraph embedding. The hypergraph data model allows us to represent seamlessly all the possible and complex interactions between users and songs with the related characteristics; meanwhile, embedding techniques provide a powerful way to infer the user-song similarities by vector mapping. We have experimented the effectiveness and efficiency of our approach with respect to the state-of-the-art most recent music recommender systems, exploiting the Million Song dataset. The results show that HEMR significantly outperforms other state-of-the-art techniques, especially in scenarios where the cold-start problem arises, thus making our system a suitable solution to embed within a music streaming platform.

Nowadays, Recommender Systems have become essential to users for finding “what they need” within large collections of items. Meanwhile, recent studies have demonstrated as user personality can effectively provide a more valuable information to significantly improve recommenders' performance, especially considering behavioural data captured from social network logs. In this work, we describe a novel music recommendation technique based on the identification of personality traits, moods and emotions of a single user, starting from solid psychological observations recognized by the analysis of user behavior within a social environment. In particular, users personality and mood have been embedded within a content-based filtering approach to obtain more accurate and dynamic results. Several experiments are then reported to show effectiveness of user personality and mood recognition recommendation, thus encouraging research in this direction.