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Nicola Barbieri
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Publications
- 2021
- [j8]Giuseppe Manco, Ettore Ritacco, Nicola Barbieri:
A Factorization Approach for Survival Analysis on Diffusion Networks. IEEE Trans. Knowl. Data Eng. 33(1): 1-13 (2021) - 2017
- [j6]Nicola Barbieri, Francesco Bonchi, Giuseppe Manco:
Efficient Methods for Influence-Based Network-Oblivious Community Detection. ACM Trans. Intell. Syst. Technol. 8(2): 32:1-32:31 (2017) - [c28]Nicola Barbieri, Giuseppe Manco, Ettore Ritacco:
Survival Factorization on Diffusion Networks. ECML/PKDD (1) 2017: 684-700 - 2014
- [b1]Nicola Barbieri, Giuseppe Manco, Ettore Ritacco:
Probabilistic Approaches to Recommendations. Synthesis Lectures on Data Mining and Knowledge Discovery, Morgan & Claypool Publishers 2014, ISBN 978-3-031-00778-1 - [c19]Nicola Barbieri, Francesco Bonchi, Giuseppe Manco:
Who to follow and why: link prediction with explanations. KDD 2014: 1266-1275 - 2013
- [j2]Nicola Barbieri, Francesco Bonchi, Giuseppe Manco:
Topic-aware social influence propagation models. Knowl. Inf. Syst. 37(3): 555-584 (2013) - [j1]Nicola Barbieri, Giuseppe Manco, Ettore Ritacco, Marco Carnuccio, Antonio Bevacqua:
Probabilistic topic models for sequence data. Mach. Learn. 93(1): 5-29 (2013) - [c17]Nicola Barbieri, Francesco Bonchi, Giuseppe Manco:
Influence-Based Network-Oblivious Community Detection. ICDM 2013: 955-960 - [c15]Nicola Barbieri, Francesco Bonchi, Giuseppe Manco:
Towards Topic-aware Social Influence Propagation Models. SEBD 2013: 199-206 - [c14]Nicola Barbieri, Francesco Bonchi, Giuseppe Manco:
Cascade-based community detection. WSDM 2013: 33-42 - 2012
- [c13]Nicola Barbieri, Antonio Bevacqua, Marco Carnuccio, Giuseppe Manco, Ettore Ritacco:
Probabilistic Sequence Modeling for Recommender Systems. KDIR 2012: 75-84 - [c12]Nicola Barbieri, Francesco Bonchi, Giuseppe Manco:
Topic-Aware Social Influence Propagation Models. ICDM 2012: 81-90 - [c11]Nicola Barbieri, Giuseppe Manco, Riccardo Ortale, Ettore Ritacco:
Balancing Prediction and Recommendation Accuracy: Hierarchical Latent Factors for Preference Data. SDM 2012: 1035-1046 - [c10]Nicola Barbieri, Giuseppe Manco, Ettore Ritacco:
Hierarchical Latent Factors for Preference Data. SEBD 2012: 251-256 - 2011
- [c8]Nicola Barbieri, Gianni Costa, Giuseppe Manco, Ettore Ritacco:
Characterizing Relationships through Co-clustering - A Probabilistic Approach. KDIR 2011: 64-73 - [c7]Nicola Barbieri, Gianni Costa, Giuseppe Manco, Ettore Ritacco:
A Block Coclustering Model for Pattern Discovering in Users' Preference Data. IC3K 2011: 94-108 - [c6]Nicola Barbieri, Giuseppe Manco:
An Analysis of Probabilistic Methods for Top-N Recommendation in Collaborative Filtering. ECML/PKDD (1) 2011: 172-187 - [c5]Nicola Barbieri, Gianni Costa, Giuseppe Manco, Riccardo Ortale:
Modeling item selection and relevance for accurate recommendations: a bayesian approach. RecSys 2011: 21-28 - [c4]Nicola Barbieri, Giuseppe Manco, Ettore Ritacco:
A Probabilistic Hierarchical Approach for Pattern Discovery in Collaborative Filtering Data. SDM 2011: 630-641 - [c3]Nicola Barbieri, Giuseppe Manco, Ettore Ritacco:
A Probabilistic Hierarchical Approach for Pattern Discovery in Collaborative Filtering Data (Extended Abstract). SEBD 2011: 239-246 - 2010
- [c2]Nicola Barbieri, Massimo Guarascio, Giuseppe Manco:
A Block Mixture Model for Pattern Discovery in Preference Data. ICDM Workshops 2010: 1100-1107
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