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Neural Recommender Systems: Theory, Methods, and Applications

Course at ESSAI 2025 - 3rd European Summer School on Artificial Intelligence

Abstract

This course provides a comprehensive overview of neural recommender systems, focusing on their foundations, state-of-the-art advancements, and practical challenges. Students will explore fundamental architectures, deep learning techniques, and the role of neural networks in personalization and recommendation tasks.

The lectures will include critical topics such as sequence-based recommendations, graph-based approaches, multi-modal systems, and fairness and explainability in recommendations. Hands-on examples and discussions will emphasize both theoretical insights and practical implementations.

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.

Schedule

9:00 - 10:30, Monday 30 to Thursday 3

Materials

TBD

Tentative Outline:

Lecture 1: Foundations of Neural Recommender Systems

Overview of traditional recommender systems

Key challenges in recommendation tasks

Introduction to deep learning for recommendations

Lecture 2: Sequence-based and Contextual Recommendations

Sequence-aware models (Markov chains, RNNs)

Contextual embeddings and attention mechanisms (Transformers)

Lecture 3: Graph-based Neural Recommender Systems

Graph Neural Networks for recommendation

Applications of graph-based models in social and item graphs

Lecture 4: Multi-modal and Domain-specific Recommendations

Integrating textual, visual, and temporal data

Case studies in e-commerce, streaming, and education

Lecture 5: Fairness, Robustness, Explainability, and Future Directions

Addressing bias and fairness in neural recommenders

Methods for explainable recommendations

Open problems and research directions

Lecturers

⁠

⁠

Giulia Di Teodoro

Federico Siciliano

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