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,Synthetic Sequence Generator for Recommender Systems – Memory Biased Random Walk on a Sequence Multa memory biased random walk model on a multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the generated synthetic data in training recommender system models in cases when privacy policies restrict clickstream publishing.

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es surrounding reserves management, portfolio construction techniques appropriate for public investors and an in-depth discussion of the challenges to achieving international diversification..978-3-030-07972-7978-3-319-90245-6

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S. Sánchez,T. de Miguel,T. G. Villa,A. R. Gorringe,I. M. Feaversmethod generates STREL formulas of bounded description complexity using a novel decision-tree approach which generalizes previous unsupervised learning techniques for Signal Temporal Logic. We demonstrate the effectiveness of our approach on case studies from diverse domains such as urban transporta