9 papers
A Foundation Model for Multimodal Event Sequences in Financial Applications
Nikita Rusakov, Vladislav Meshkov, Konstantin Zorin +4
Predictive modeling is a core component of modern financial services, where a wide range of tasks are traditionally addressed using separate models trained on manually engineered t…
Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs
Maxim Zhelnin, Dmitry Redko, Daniil Volkov +8
Sequential recommendations (SR) with transformer-based architectures are widely adopted in real-world applications, where SR models require frequent retraining to adapt to ever-cha…
Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation
Nikita Severin, Danil Kartushov, Vladislav Urzhumov +8
Sequential recommender systems have achieved significant success in modeling temporal user behavior but remain limited in capturing rich user semantics beyond interaction patterns.…
SplitLight: An Exploratory Toolkit for Recommender Systems Datasets and Splits
Anna Volodkevich, Dmitry Anikin, Danil Gusak +3
Offline evaluation of recommender systems is often affected by hidden, under-documented choices in data preparation. Seemingly minor decisions in filtering, handling repeats, cold-…
Sparse Autoencoders for Sequential Recommendation Models: Interpretation and Flexible Control
Anton Klenitskiy, Konstantin Polev, Daria Denisova +3
Many current state-of-the-art models for sequential recommendations are based on transformer architectures. Interpretation and explanation of such black box models is an important…
Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns
Veronika Ivanova, Evgeny Frolov, Alexey Vasilev
We consider the task of learning from both positive and negative feedback in a sequential recommendation scenario, as both types of feedback are often present in user interactions.…