Title: Transformer-based Sequential Recommender Systems
Instructors: Jan Malte Lichtenberg, Aleksandr V. Petrov
Transformer-based models have become the cornerstone of sequential recommendation, yet they are often perceived either as rigid engineering recipes or as a collection of disconnected architectures. This tutorial demystifies these systems by centering on a provocative guiding question: ``What is not sequential recommendation?'' By framing recommendation as an inherently temporal task, where user intent, catalogs, and context constantly evolve, we present Transformers as a unified and practical framework for modern Recommender Systems.
The tutorial is structured into two blocks. The first block establishes foundations using SASRec as a running base model, covering item embeddings, causal self-attention, and the impact of positional information. The second block dives into advanced trade-offs, comparing causal and masked (BERT4Rec) modeling, scaling to large catalogs via quantization and Seamtatic IDs, and integrating content-aware representations (DenseRec).
Designed for both researchers and industry practitioners, this session prioritises intuition and reproducibility. Participants will interact with a live playground to observe how model assumptions affect real-time recommendations. Attendees will leave with a clear understanding of when to use Transformer-based models, how to navigate their architectural trade-offs, and a suite of open-source materials for robust implementation.