3 papers
cs.IR2026
VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions
Katya Mirylenka, Egor Malykh, Mahdyar Ravanbakhsh +10
The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds. This transition introduces an ``extreme cold-start'' problem: unli…
cs.LG2026
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…
cs.LG2025
Short-Form Video Recommendations with Multimodal Embeddings: Addressing Cold-Start and Bias Challenges
Andrii Dzhoha, Katya Mirylenka, Egor Malykh +2
In recent years, social media users have spent significant amounts of time on short-form video platforms. As a result, established platforms in other domains, such as e-commerce, h…