Kirell Benzi

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In this work, we propose a fast, robust and scalable method for retrieving and analyzing recurring patterns of activity induced by a causal process, typically modeled as time series on a graph. We introduce a particular type of multilayer graph as a model for the data. This graph is structured for emphasizing causal relations between connected nodes and(More)
This work formulates a novel song recommender system 1 as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode both playlist proximity information and song similarity, using a rich combination of audio,(More)
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