46 citations · 86 across the 15 of their papers we have counts for
15 papers
Hypothesis-Driven Shelf Generation for Personalised Recommendation
Aleksandr V. Petrov, Tarun Chillara, Matthew D. Moellman +13
Modern recommendation interfaces organise content into shelves: themed rows such as "More of What You Like" or "New Releases for You." In production systems, these shelves are typi…
URecJPQ: Memory-efficient Multimodal Recommendation Models through RecJPQ in Large-Scale Scenarios
Giuseppe Spillo, Zixuan Yi, Aleksandr Petrov +3
Training state-of-the-art recommendation models on large-scale industrial datasets can be a challenging task due to the high number of users and items which are typically represent…
eSASRec: Enhancing Transformer-based Recommendations in a Modular Fashion
Daria Tikhonovich, Nikita Zelinskiy, Aleksandr V. Petrov +4
Since their introduction, Transformer-based models, such as SASRec and BERT4Rec, have become common baselines for sequential recommendations, surpassing earlier neural and non-neur…
Balancing Accuracy and Novelty with Sub-Item Popularity
Chiara Mallamaci, Aleksandr Vladimirovich Petrov, Alberto Carlo Maria Mancino +3
In the realm of music recommendation, sequential recommenders have shown promise in capturing the dynamic nature of music consumption. A key characteristic of this domain is repeti…
Efficient Recommendation with Millions of Items by Dynamic Pruning of Sub-Item Embeddings
Aleksandr V. Petrov, Craig Macdonald, Nicola Tonellotto
A large item catalogue is a major challenge for deploying modern sequential recommender models, since it makes the memory footprint of the model large and increases inference laten…
Enhancing Sequential Music Recommendation with Personalized Popularity Awareness
Davide Abbattista, Vito Walter Anelli, Tommaso Di Noia +2
In the realm of music recommendation, sequential recommender systems have shown promise in capturing the dynamic nature of music consumption. Nevertheless, traditional Transformer-…
Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of Items
Aleksandr V. Petrov, Craig Macdonald, Nicola Tonellotto
Transformer-based recommender systems, such as BERT4Rec or SASRec, achieve state-of-the-art results in sequential recommendation. However, it is challenging to use these models in…
Shallow Cross-Encoders for Low-Latency Retrieval
Aleksandr V. Petrov, Sean MacAvaney, Craig Macdonald
Transformer-based Cross-Encoders achieve state-of-the-art effectiveness in text retrieval. However, Cross-Encoders based on large transformer models (such as BERT or T5) are comput…
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning
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…
RecJPQ: Training Large-Catalogue Sequential Recommenders
Aleksandr V. Petrov, Craig Macdonald
Sequential Recommendation is a popular recommendation task that uses the order of user-item interaction to model evolving users' interests and sequential patterns in their behaviou…
gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling
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 scor…
Generative Sequential Recommendation with GPTRec
Aleksandr V. Petrov, Craig Macdonald
Sequential recommendation is an important recommendation task that aims to predict the next item in a sequence. Recently, adaptations of language models, particularly Transformer-b…
MTS Kion Implicit Contextualised Sequential Dataset for Movie Recommendation
Aleksandr Petrov, Ildar Safilo, Daria Tikhonovich +1
We present a new movie and TV show recommendation dataset collected from the real users of MTS Kion video-on-demand platform. In contrast to other popular movie recommendation data…
A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation
Aleksandr Petrov, Craig Macdonald
BERT4Rec is an effective model for sequential recommendation based on the Transformer architecture. In the original publication, BERT4Rec claimed superiority over other available s…
Effective and Efficient Training for Sequential Recommendation using Recency Sampling
Aleksandr Petrov, Craig Macdonald
Many modern sequential recommender systems use deep neural networks, which can effectively estimate the relevance of items but require a lot of time to train. Slow training increas…