Deep & Cross Networks (DCN) are established deep learning architectures for modeling feature interactions in CTR prediction, ranking, and recommender systems. Although they can achieve metrics comparable to gradient-boosting methods, existing implementations are often organized around framework-specific training loops...
Aleksandr Milogradskii, Ilya Veselov, Yaroslav Klyukin et al.· Proceedings of the 20th ACM...· 0 citations
We introduce T-ECD, an open large-scale dataset for recommender systems research, containing over 135 billion interactions from 44 million users and 30 million items. The dataset is derived from anonymized real-world data within a unified banking ecosystem and spans five interconnected e-commerce domains: marketplace,...
Kiryl Liakhnovich, Анна Федоровна Никифорова, Nikita Matveev et al.· Proceedings of the 32nd ACM...· 0 citations
LLM-enhanced linear autoencoders (L3AE) incorporate semantic item representations from large language models into collaborative filtering and demonstrate significant gains on long-tail items. However, L3AE requires dense n × n matrices, which makes it impractical for large catalogs — exactly where semantic enrichment w...
Maxim Skurikhin, Kiryl Liakhnovich, Oleg Lashinin· Proceedings of the 20th ACM...· 0 citations
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