13 citations · 38 across the 8 of their papers we have counts for
8 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-…
CoRoVA: Compressed Representations for Vector-Augmented Code Completion
Daria Cherniuk, Nikita Sukhorukov, Danil Gusak +4
Retrieval-augmented generation has emerged as one of the most effective approaches for code completion enhancement, especially when repository-level context is important. However,…
Recommendation Is a Dish Better Served Warm
Danil Gusak, Nikita Sukhorukov, Evgeny Frolov
In modern recommender systems, experimental settings typically include filtering out cold users and items based on a minimum interaction threshold. However, these thresholds are of…
Maximum Impact with Fewer Features: Efficient Feature Selection for Cold-Start Recommenders through Collaborative Importance Weighting
Nikita Sukhorukov, Danil Gusak, Evgeny Frolov
Cold-start challenges in recommender systems necessitate leveraging auxiliary features beyond user-item interactions. However, the presence of irrelevant or noisy features can degr…
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…
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…
Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs
Gleb Mezentsev, Danil Gusak, Ivan Oseledets +1
Scalability issue plays a crucial role in productionizing modern recommender systems. Even lightweight architectures may suffer from high computational overload due to intermediate…
RECE: Reduced Cross-Entropy Loss for Large-Catalogue Sequential Recommenders
Danil Gusak, Gleb Mezentsev, Ivan Oseledets +1
Scalability is a major challenge in modern recommender systems. In sequential recommendations, full Cross-Entropy (CE) loss achieves state-of-the-art recommendation quality but con…