1 code implementation • 7 Mar 2024 • Aleksandr Petrov, Craig Macdonald
Adaptations of Transformer models, such as BERT4Rec and SASRec, achieve state-of-the-art performance in the sequential recommendation task according to accuracy-based metrics, such as NDCG.
2 code implementations • 14 Aug 2023 • Aleksandr Petrov, Craig Macdonald
A large catalogue size is one of the central challenges in training recommendation models: a large number of items makes them memory and computationally inefficient to compute scores for all items during training, forcing these models to deploy negative sampling.
no code implementations • 1 Sep 2022 • Aleksandr Petrov, Ildar Safilo, Daria Tikhonovich, Dmitry Ignatov
We present a new movie and TV show recommendation dataset collected from the real users of MTS Kion video-on-demand platform.
1 code implementation • 15 Jul 2022 • Aleksandr Petrov, Craig Macdonald
We also propose our own implementation of BERT4Rec based on the Hugging Face Transformers library, which we demonstrate replicates the originally reported results on 3 out 4 datasets, while requiring up to 95% less training time to converge.
1 code implementation • 6 Jul 2022 • Aleksandr Petrov, Craig Macdonald
Hence, we propose a novel Recency-based Sampling of Sequences training objective that addresses both limitations.
no code implementations • 23 Mar 2021 • Aleksandr Petrov, Yuriy Makarov
This paper describes an approach to solving the next destination city recommendation problem for a travel reservation system.