Francesco Barile (F.)

Motamedi, E., Kholgh, D. K., Saghari, S., Elahi, M., Barile, F., & Tkalcic, M. (2024). Predicting movies’ eudaimonic and hedonic scores: A machine learning approach using metadata, audio and visual features. Information Processing & Management, 61(2), Article 103610. https://doi.org/10.1016/j.ipm.2023.103610

Zilbershtein, D., Barile, F., Odijk, D., & Tintarev, N. (2024). Bridging the Transparency Gap: Exploring Multi-Stakeholder Preferences for Targeted Advertisement Explanations. CEUR Workshop Proceedings, 3815, 46-58. https://ceur-ws.org/Vol-3815/paper4.pdf

Massimo, D., Ganthaler, E., Buriro, A., Barile, F., Moraschini, M., Dignös, A., Villgrattner, T., Peer, A., & Ricci, F. (2023). Estimation of Mass and Lengths of Sintered Workpieces Using Machine Learning Models. Ieee Transactions on Instrumentation and Measurement, 72, Article 2522414. https://doi.org/10.1109/TIM.2023.3298413

Barile, F., Draws, T., Inel, O., Rieger, A., Najafian, S., Ebrahimi Fard, A., Hada, R., & Tintarev, N. (2023). Evaluating explainable social choice-based aggregation strategies for group recommendation. User Modeling and User-Adapted Interaction, 34(1), 1-58. https://doi.org/10.1007/s11257-023-09363-0