23 citations · 63 across the 9 of their papers we have counts for
9 papers
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-…
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…
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.…
Autoregressive Generation Strategies for Top-K Sequential Recommendations
Anna Volodkevich, Danil Gusak, Anton Klenitskiy +1
The goal of modern sequential recommender systems is often formulated in terms of next-item prediction. In this paper, we explore the applicability of generative transformer-based…
Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendations
Anton Klenitskiy, Anna Volodkevich, Anton Pembek +1
Sequential recommender systems are an important and demanded area of research. Such systems aim to use the order of interactions in a user's history to predict future interactions.…
From Variability to Stability: Advancing RecSys Benchmarking Practices
Valeriy Shevchenko, Nikita Belousov, Alexey Vasilev +6
In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily…
Synthetic Data-Based Simulators for Recommender Systems: A Survey
Elizaveta Stavinova, Alexander Grigorievskiy, Anna Volodkevich +3
This survey aims at providing a comprehensive overview of the recent trends in the field of modeling and simulation (M&S) of interactions between users and recommender systems and…