• Ferraro A, Ekstrand M and Bauer C. It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation. Proceedings of the 18th ACM Conference on Recommender Systems. (884-889).

    https://doi.org/10.1145/3640457.3688163

  • Wang S, Zhang X, Wang Y and Ricci F. (2023). Trustworthy Recommender Systems. ACM Transactions on Intelligent Systems and Technology. 15:4. (1-20). Online publication date: 31-Aug-2024.

    https://doi.org/10.1145/3627826

  • Coppolillo E, Manco G and Gionis A. Relevance Meets Diversity: A User-Centric Framework for Knowledge Exploration Through Recommendations. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (490-501).

    https://doi.org/10.1145/3637528.3671949

  • Chee J, Kalyanaraman S, Ernala S, Weinsberg U, Dean S and Ioannidis S. Harm Mitigation in Recommender Systems under User Preference Dynamics. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (255-265).

    https://doi.org/10.1145/3637528.3671925

  • An G, Sun J, Yang Y and Sun F. (2024). Enhancing Collaborative Information with Contrastive Learning for Session-based Recommendation. Information Processing and Management: an International Journal. 61:4. Online publication date: 1-Jul-2024.

    https://doi.org/10.1016/j.ipm.2024.103738

  • Cai Y and Wang F. Exploring the Behavior of Users “Training” Douyin’s Personalized Recommendation Algorithm System in China. Human Interface and the Management of Information. (189-208).

    https://doi.org/10.1007/978-3-031-60114-9_14

  • Hazrati N and Ricci F. (2024). Choice models and recommender systems effects on users’ choices. User Modeling and User-Adapted Interaction. 34:1. (109-145). Online publication date: 1-Mar-2024.

    https://doi.org/10.1007/s11257-023-09366-x

  • Ruan Q, Mac Namee B and Dong R. Unveiling the Relationship Between News Recommendation Algorithms and Media Bias: A Simulation-Based Analysis of the Evolution of Bias Prevalence. Artificial Intelligence XL. (210-215).

    https://doi.org/10.1007/978-3-031-47994-6_17

  • Cavenaghi E, Sottocornola G, Stella F and Zanker M. (2023). A Systematic Study on Reproducibility of Reinforcement Learning in Recommendation Systems. ACM Transactions on Recommender Systems. 1:3. (1-23). Online publication date: 30-Sep-2023.

    https://doi.org/10.1145/3596519

  • Piliponyte G, Massimo D and Ricci F. The Impact of Personalised Advertisement Campaigns on Tourist Choices in South Tyrol: A Sustainable Tourism Perspective. Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization. (100-103).

    https://doi.org/10.1145/3563359.3597445

  • Ruan Q, Mac Namee B and Dong R. The Influence of Media Bias on News Recommender Systems. Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization. (301-305).

    https://doi.org/10.1145/3565472.3595619

  • López-Santillán R, González L, Montes-y-Gómez M and López-Monroy A. (2023). When attention is not enough to unveil a text’s author profile: Enhancing a transformer with a wide branch. Neural Computing and Applications. 35:13. (9607-9626). Online publication date: 1-May-2023.

    https://doi.org/10.1007/s00521-023-08198-5

  • Tejaswi S, Sastry V and Durga Bhavani S. MCMARS: Hybrid Multi-criteria Decision-Making Algorithm for Recommender Systems of Mobile Applications. Distributed Computing and Intelligent Technology. (107-124).

    https://doi.org/10.1007/978-3-031-24848-1_8

  • Azzalini D, Azzalini F, Criscuolo C, Dolci T, Martinenghi D and Amer-Yahia S. SoCRATe: A Recommendation System with Limited-Availability Items. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. (4793-4797).

    https://doi.org/10.1145/3511808.3557208

  • Wang S, Zhang P, Wang H, Yu H and Zhang F. (2022). Detecting shilling groups in online recommender systems based on graph convolutional network. Information Processing and Management: an International Journal. 59:5. Online publication date: 1-Sep-2022.

    https://doi.org/10.1016/j.ipm.2022.103031

  • Hazrati N and Ricci F. Simulating Users’ Interactions with Recommender Systems. Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. (95-98).

    https://doi.org/10.1145/3511047.3536402

  • Jannach D, Pu P, Ricci F and Zanker M. (2022). Recommender systems. AI Magazine. 43:2. (145-150). Online publication date: 23-Jun-2022.

    https://doi.org/10.1002/aaai.12050