4 papers
Long-Term Embeddings for Balanced Personalization
Andrii Dzhoha, Egor Malykh
Modern transformer-based sequential recommenders excel at capturing short-term intent but often suffer from recency bias, overlooking stable long-term preferences. While extending…
Efficient and Effective Query Context-Aware Learning-to-Rank Model for Sequential Recommendation
Andrii Dzhoha, Alisa Mironenko, Evgeny Labzin +3
Modern sequential recommender systems commonly use transformer-based models for next-item prediction. While these models demonstrate a strong balance between efficiency and quality…
Reducing Popularity Influence by Addressing Position Bias
Andrii Dzhoha, Alexey Kurennoy, Vladimir Vlasov +1
Position bias poses a persistent challenge in recommender systems, with much of the existing research focusing on refining ranking relevance and driving user engagement. However, i…
Building a Scalable, Effective, and Steerable Search and Ranking Platform
Marjan Celikik, Jacek Wasilewski, Ana Peleteiro Ramallo +7
Modern e-commerce platforms offer vast product selections, making it difficult for customers to find items that they like and that are relevant to their current session intent. Thi…