Speakers: Giulia Di Teodoro (University of Pisa) and Federico Siciliano (Sapienza University of Rome)

Title: Neural Recommender Systems: Theory, Methods, and Applications

Date|Time: March 26-28, 2025

Place: NOVA FCT, Edifício da Biblioteca, Sala Multiusos

Description: This course provides a comprehensive overview of neural recommender systems, focusing on their foundations, state-of-the-art advancements, and practical challenges. It covers essential deep learning architectures and techniques that power modern recommender systems, exploring their role in personalizing user experiences across various domains such as e-commerce, entertainment, and social media. Key topics include sequence-based models, which capture sequential user behavior, and Transformer-based approaches, which leverage attention mechanisms for better context understanding. The course also delves into graph-based recommendation models, which model relationships between users and items in complex networks. By the end of the course, students will gain a strong understanding of neural recommender systems, their applications, and the challenges of deploying them in real-world settings. Additionally, the course includes an online hands-on session where participants will implement recommender system models in Python. To fully benefit from the hands-on session, participants should have basic knowledge of Python programming, familiarity with machine learning concepts, and an understanding of deep learning fundamentals (e.g. neural networks and backpropagation). Experience with PyTorch is recommended but not required.

Registration can be completed here.

Course organization:

 

  1. Lecture 1- 26th March (15h-17h)
    Foundations of Neural Recommender Systems

 1.1.     Introduction to Recommender Systems

 1.2.     Foundations of Neural Recommender Systems

  1. Lecture 2- 27th March (10h-12h) 
    Sequence-based and Contextual Recommendations

 2.1.     Sequence-aware models

 2.2.     Graph Neural Networks for recommendation

  1. Lecture 3- 27th March (14h-16h)
    Evaluation of Recommender Systems and Explainability and Fairness

 3.1.     Best practices for offline evaluation

 3.2.     Explainability techniques and way for mitigating exposure bias

  1. Lecture 4 – 28th March (10h-12h; 14h-16h)
    Online hands-on sessions

 4.1.     Practice on implementation of recommender system models in Python via remote lectures. 

Other info: 

Expected Level and Prerequisites
The course is designed for graduate students and researchers with a foundational understanding of machine learning and deep learning. Familiarity with basic neural network architectures (e.g., CNNs, RNNs) is recommended.

Will the course appeal to students outside of the main area of the course?
Yes, the course will appeal to students from related areas such as natural language processing, and computer vision, and optimization, given its focus on domain-specific applications. The emphasis on ethical considerations and explainability will also interest students working on Safe, Explainable, and Trustworthy AI.

Giulia Di Teodoro is currently a postdoctoral researcher at the University of Pisa, specializing in machine learning and information-filtering systems. She graduated with honors in Management engineering from Sapienza University of Rome, Italy, where she also completed her PhD in Data Science in 2024, with a thesis on precision medicine for HIV and diabetes and interpretability of machine learning models. She has publications in the domain of Precision Medicine, Explainable Artificial Intelligence and Bioinformatics. Her academic journey includes extensive international exposure, such as a visiting researcher position at the Uniklinik Köln in Germany, in collaboration with the  Max Planck Institutes. Her research interests encompass Mixed-Integer Linear Programming (MILP), Precision medicine and Recommendation Systems. 

Federico Siciliano is a postdoctoral researcher at Sapienza University of Rome, specializing in information retrieval and recommender systems. He completed his PhD in Data Science at Sapienza University in 2024, with a thesis on the architectural components of trustworthy artificial intelligence, earning the distinction of “excellent with honors”.

Federico’s professional experience includes a research internship at Amazon Italy. He has also contributed to various research grants. 

As an active member of the academic community, Federico has served as a PC member for various conferences and organized workshops at both SIGIR and RecSys. His expertise spans recommender systems, robust AI, and trustworthy models, evidenced by publications in top-tier venues such as RecSys, SIGIR, and IJCNN.

References:

  1. Introduction to Recommender Systems
  2. Shapira, B., Rokach, L., & Ricci, F. (2022). Recommender systems handbook.
  3. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017, April). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173-182)
  4. Rendle, S., Krichene, W., Zhang, L., & Anderson, J. (2020, September). Neural collaborative filtering vs. matrix factorization revisited. In Proceedings of the 14th ACM Conference on Recommender Systems (pp. 240-248).
  5. Sequence-based and Graph-based Neural Recommender Systems
  6. Quadrana, M., Cremonesi, P., & Jannach, D. (2018). Sequence-aware recommender systems. ACM computing surveys (CSUR), 51(4), 1-36.
  7. Gao, C., Zheng, Y., Li, N., Li, Y., Qin, Y., Piao, J., … & Li, Y. (2023). A survey of graph neural networks for recommender systems: Challenges, methods, and directions. ACM Transactions on Recommender Systems, 1(1), 1-51
  8. Betello, F., Purificato, A., Siciliano, F., Trappolini, G., Bacciu, A., Tonellotto, N., & Silvestri, F. (2024). A Reproducible Analysis of Sequential Recommender Systems. IEEE Access.
  9. Betello, F., Siciliano, F., Mishra, P., & Silvestri, F. (2024, March). Investigating the Robustness of Sequential Recommender Systems Against TrainingData Perturbations. In European Conference on Information Retrieval (pp. 205-220). Cham: Springer Nature Switzerland.
  10. Evaluation of Recommender Systems and Explainability and Fairness
  11. Zangerle, E., & Bauer, C. (2022). Evaluating recommender systems: survey and framework. ACM Computing Surveys, 55(8), 1-38.
  12. Beel, J., Jannach, D., Said, A., Shani, G., Vente, T., & Wegmeth, L. (2022). 4.3 Best-Practices for Offline Evaluations of Recommender Systems. Evaluation Perspectives of Recommender Systems: Driving Research and Education, 55(8), 110.
  13. Deldjoo, Y., Di Noia, T., Di Sciascio, E., & Merra, F. A. (2020, July). How dataset characteristics affect the robustness of collaborative recommendation models. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 951-960).