DeepRecSys, лекция 6: Нейросетевое ранжирование II
Автор: InformationRetriever
Загружено: 2026-03-11
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Lecture: Kirill Khrylchenko
Seminar: Artem Matveev
This week we continue our exploration of deep learning–based ranking models.
During the lecture, we discuss:
1. Feature interaction modeling — from linear models and factorization techniques to modern architectures such as DCN-v2 and transformer-based interaction models
2. Deep layers — what architectures are actually used in practice instead of plain MLPs (spoiler: ResNets and DenseNets)
3. Multi-task learning for ranking — including the Multi-gate Mixture-of-Experts (MMoE) architecture and the Entire Space Multi-Task Model (ESMM)
4. Positional bias and the main techniques used to mitigate it
5. Knowledge distillation and how it is applied in large-scale ranking systems
During the seminar, we have an in-depth discussion of the DCN-v2 paper - DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems.
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