5 papers
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…
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-…
Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders
Danil Gusak, Anna Volodkevich, Anton Klenitskiy +2
Modern sequential recommender systems, ranging from lightweight transformer-based variants to large language models, have become increasingly prominent in academia and industry due…
RePlay: a Recommendation Framework for Experimentation and Production Use
Alexey Vasilev, Anna Volodkevich, Denis Kulandin +2
Using a single tool to build and compare recommender systems significantly reduces the time to market for new models. In addition, the comparison results when using such tools look…
Neural Click Models for Recommender Systems
Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev +4
We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models.…