7 citations · 10 across the 3 of their papers we have counts for
3 papers
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…
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…