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2020 – today
- 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)
2010 – 2019
- 2018
- [j7]Fidel Cacheda, Roi Blanco, Nicola Barbieri:
Characterizing and Predicting Users' Behavior on Local Search Queries. ACM Trans. Web 12(2): 11:1-11:32 (2018) - [c29]Fidel Cacheda, Nicola Barbieri:
Click-through prediction when searching local businesses. CERI 2018: 9:1-9:2 - 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 - [c27]Luca Maria Aiello, Nicola Barbieri:
Evolution of Ego-networks in Social Media with Link Recommendations. WSDM 2017: 111-120 - [c26]Fidel Cacheda, Nicola Barbieri, Roi Blanco:
Click Through Rate Prediction for Local Search Results. WSDM 2017: 171-180 - [c25]Yandi Li, Nicola Barbieri, Daniele Quercia:
Applying Space Syntax to Online Mapping Tools. WSDM 2017: 273-282 - [i2]Luca Maria Aiello, Nicola Barbieri:
Evolution of Ego-networks in Social Media with Link Recommendations. CoRR abs/1702.01398 (2017) - 2016
- [j5]Yasir Mehmood, Nicola Barbieri, Francesco Bonchi:
Modeling adoptions and the stages of the diffusion of innovations. Knowl. Inf. Syst. 48(1): 1-27 (2016) - [c24]Luca Maria Aiello, Ioannis Arapakis, Ricardo Baeza-Yates, Xiao Bai, Nicola Barbieri, Amin Mantrach, Fabrizio Silvestri:
The Role of Relevance in Sponsored Search. CIKM 2016: 185-194 - [c23]Ya Le, Eric Bax, Nicola Barbieri, David García-Soriano, Jitesh Mehta, James Li:
Validation of matching. IJCNN 2016: 4457-4464 - [c22]Nicola Barbieri, Fabrizio Silvestri, Mounia Lalmas:
Improving Post-Click User Engagement on Native Ads via Survival Analysis. WWW 2016: 761-770 - 2015
- [j4]Nicola Barbieri, Francesco Bonchi, Edoardo Galimberti, Francesco Gullo:
Efficient and effective community search. Data Min. Knowl. Discov. 29(5): 1406-1433 (2015) - 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 - [j3]Elena Demidova, Nicola Barbieri, Stefan Dietze, Adam Funk, Helge Holzmann, Diana Maynard, Nikolaos Papailiou, Wim Peters, Thomas Risse, Dimitris Spiliotopoulos:
Analysing and Enriching Focused Semantic Web Archives for Parliament Applications. Future Internet 6(3): 433-456 (2014) - [c21]Çigdem Aslay, Nicola Barbieri, Francesco Bonchi, Ricardo Baeza-Yates:
Online Topic-aware Influence Maximization Queries. EDBT 2014: 295-306 - [c20]Yasir Mehmood, Nicola Barbieri, Francesco Bonchi:
Modeling Adoptions and the Stages of the Diffusion of Innovations. ICDM 2014: 420-429 - [c19]Nicola Barbieri, Francesco Bonchi, Giuseppe Manco:
Who to follow and why: link prediction with explanations. KDD 2014: 1266-1275 - [c18]Nicola Barbieri, Francesco Bonchi:
Influence Maximization with Viral Product Design. SDM 2014: 55-63 - [i1]Ya Le, Eric Bax, Nicola Barbieri, David García-Soriano, Jitesh Mehta, James Li:
Validation of Network Reconciliation. CoRR abs/1411.0023 (2014) - 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 - [c16]Yasir Mehmood, Nicola Barbieri, Francesco Bonchi, Antti Ukkonen:
CSI: Community-Level Social Influence Analysis. ECML/PKDD (2) 2013: 48-63 - [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
- [c9]Nicola Barbieri:
Regularized Gibbs Sampling for User Profiling with Soft Constraints. ASONAM 2011: 129-136 - [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 - [c1]Nicola Barbieri, Massimo Guarascio, Ettore Ritacco:
An Empirical Comparison of Collaborative Filtering Approaches on Netflix Data. IIR 2010: 23-27
Coauthor Index
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