Francesco Barile (F.)
Key publications
-
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/s11257-023-09363-0More information about this publication
-
Barile, F., Cau, F. M., & Tintarev, N. (2024). A Preliminary Analysis on Self and Peer Evaluation of Personality Models for Recommender Systems. In UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 70-74). Association for Computing Machinery (ACM). https://doi.org/10.1145/3631700.3664864More information about this publication
-
Barile, F., Cau, F. M., & Tintarev, N. (2024). A Preliminary Study of the Impact of Personality on Satisfaction in Group Contexts. In UMAP 2024 - Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (pp. 319-328). Association for Computing Machinery (ACM). https://doi.org/10.1145/3631700.3664893More information about this publication
-
Schellingerhout, R., Barile, F., & Tintarev, N. (2024). Creating Healthy Friction: Determining Stakeholder Requirements of Job Recommendation Explanations. In Recommender Systems for Human Resources 2024: Proceedings of the 4th Workshop on Recommender Systems for Human Resources (RecSys-in-HR 2024) (Vol. 3788). CEUR-WS.org. https://arxiv.org/abs/2409.15971More information about this publication
-
Granada, M. G., Zilbershtein, D., Odijk, D., & Barile, F. (2023). VideolandGPT: A User Study on a Conversational Recommender System. CEUR Workshop Proceedings, 3560, 44-49.More information about this publication
-
Schellingerhout, R., Barile, F., & Tintarev, N. (2023). A Co-design Study for Multi-stakeholder Job Recommender System Explanations. In L. Longo (Ed.), Explainable Artificial Intelligence - 1st World Conference, xAI 2023, Proceedings: Proceedings, Part II (pp. 597-620). Springer. https://doi.org/10.1007/978-3-031-44067-0_30More information about this publication
-
Wu, Z., Draws, T., Cau, F., Barile, F., Rieger, A., & Tintarev, N. (2023). Explaining Search Result Stances to Opinionated People. In L. Longo (Ed.), Explainable Artificial Intelligence (pp. 573-596). Springer. https://doi.org/10.1007/978-3-031-44067-0_29More information about this publication
-
Najafian, S., Draws, T., Barile, F., Tkalcic, M., Yang, J., & Tintarev, N. (2021). Exploring User Concerns about Disclosing Location and Emotion Information in Group Recommendations. In HT '21: Proceedings of the 32nd ACM Conference on Hypertext and Social Media (pp. 155-164). Association for Computing Machinery. https://doi.org/10.1145/3465336.3475104More information about this publication
-
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.2023.103610More information about this publication
-
van Kuijk, K., Mahmoudi, S. S., Wen, Y., Barile, F., & Rienstra, T. (2023). An Argumentative Framework for Generating Explainable Group Recommendations. In Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (pp. 266–274). Association for Computing Machinery. https://doi.org/10.1145/3563359.3597387More information about this publication