Search Results for author: Aleksandr Petrov

Found 6 papers, 4 papers with code

Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning

1 code implementation7 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.

reinforcement-learning Re-Ranking +1

gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling

2 code implementations14 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.

Sequential Recommendation

MTS Kion Implicit Contextualised Sequential Dataset for Movie Recommendation

no code implementations1 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.

Movie Recommendation Recommendation Systems

A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation

1 code implementation15 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.

Sequential Recommendation

Effective and Efficient Training for Sequential Recommendation using Recency Sampling

1 code implementation6 Jul 2022 Aleksandr Petrov, Craig Macdonald

Hence, we propose a novel Recency-based Sampling of Sequences training objective that addresses both limitations.

Sequential Recommendation

Attention-based neural re-ranking approach for next city in trip recommendations

no code implementations23 Mar 2021 Aleksandr Petrov, Yuriy Makarov

This paper describes an approach to solving the next destination city recommendation problem for a travel reservation system.

Learning-To-Rank Re-Ranking

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