8 citations · 14 across the 6 of their papers we have counts for
12 papers · 1 filter
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-…