From the 1 of 5 linked papers with an AI index.
5 papers
Hypothesis-Driven Shelf Generation for Personalised Recommendation
Aleksandr V. Petrov, Tarun Chillara, Matthew D. Moellman +13
The paper introduces a system for generating personalized recommendation shelves on Spotify by using natural‑language hypotheses to guide content selection, combining hypothesis ge…
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…