57 citations · 163 across the 10 of their papers we have counts for
10 papers
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…
Stalactite: Toolbox for Fast Prototyping of Vertical Federated Learning Systems
Anastasiia Zakharova, Dmitriy Alexandrov, Maria Khodorchenko +4
Machine learning (ML) models trained on datasets owned by different organizations and physically located in remote databases offer benefits in many real-world use cases. State regu…
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…
Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization
Abdulaziz Samra, Evgeney Frolov, Alexey Vasilev +2
Data sparsity has been one of the long-standing problems for recommender systems. One of the solutions to mitigate this issue is to exploit knowledge available in other source doma…
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…
Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?
Anton Klenitskiy, Alexey Vasilev
Recently sequential recommendations and next-item prediction task has become increasingly popular in the field of recommender systems. Currently, two state-of-the-art baselines are…
The Long Tail of Context: Does it Exist and Matter?
Konstantin Bauman, Alexey Vasilev, Alexander Tuzhilin
Context has been an important topic in recommender systems over the past two decades. A standard representational approach to context assumes that contextual variables and their st…
Quality Metrics in Recommender Systems: Do We Calculate Metrics Consistently?
Yan-Martin Tamm, Rinchin Damdinov, Alexey Vasilev
Offline evaluation is a popular approach to determine the best algorithm in terms of the chosen quality metric. However, if the chosen metric calculates something unexpected, this…