Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Frolov, Evgeny"'
Scalability issue plays a crucial role in productionizing modern recommender systems. Even lightweight architectures may suffer from high computational overload due to intermediate calculations, limiting their practicality in real-world applications.
Externí odkaz:
http://arxiv.org/abs/2409.18721
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 consumes excessive GPU memory with large item catalogs, limiting its prac
Externí odkaz:
http://arxiv.org/abs/2408.02354
Autor:
Baikalov, Vladimir, Frolov, Evgeny
Recent recommender system advancements have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships within behavioral data, leading to promising outcomes in personalized ranki
Externí odkaz:
http://arxiv.org/abs/2403.00895
In production applications of recommender systems, a continuous data flow is employed to update models in real-time. Many recommender models often require complete retraining to adapt to new data. In this work, we introduce a novel collaborative filt
Externí odkaz:
http://arxiv.org/abs/2312.10064
Autor:
Sayapin, Albert, Balitskiy, Gleb, Bershatsky, Daniel, Katrutsa, Aleksandr, Frolov, Evgeny, Frolov, Alexey, Oseledets, Ivan, Kharin, Vitaliy
In this study, we propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. Although this problem can be represented as a classical collaborative filtering problem, it requires proper modification
Externí odkaz:
http://arxiv.org/abs/2303.04744
We introduce the novel approach towards fake text reviews detection in collaborative filtering recommender systems. The existing algorithms concentrate on detecting the fake reviews, generated by language models and ignore the texts, written by disho
Externí odkaz:
http://arxiv.org/abs/2301.03025
Autor:
Frolov, Evgeny, Oseledets, Ivan
Self-attentive transformer models have recently been shown to solve the next item recommendation task very efficiently. The learned attention weights capture sequential dynamics in user behavior and generalize well. Motivated by the special structure
Externí odkaz:
http://arxiv.org/abs/2212.05720
Autor:
Marin, Nikita, Makhneva, Elizaveta, Lysyuk, Maria, Chernyy, Vladimir, Oseledets, Ivan, Frolov, Evgeny
Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items while others almost always assign 5 stars to the chosen item. Even if the
Externí odkaz:
http://arxiv.org/abs/2205.05070
Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario collaborative information may be scarce or even unavailable, whereas the c
Externí odkaz:
http://arxiv.org/abs/2205.04490
Autor:
Chekalina, Viktoriia, Razzhigaev, Anton, Sayapin, Albert, Frolov, Evgeny, Panchenko, Alexander
Knowledge Graphs (KGs) are symbolically structured storages of facts. The KG embedding contains concise data used in NLP tasks requiring implicit information about the real world. Furthermore, the size of KGs that may be useful in actual NLP assignme
Externí odkaz:
http://arxiv.org/abs/2204.10629