Zobrazeno 1 - 10
of 643
pro vyhledávání: '"Petrov , Aleksandr"'
Autor:
Abbattista, Davide, Anelli, Vito Walter, Di Noia, Tommaso, Macdonald, Craig, Petrov, Aleksandr Vladimirovich
In the realm of music recommendation, sequential recommender systems have shown promise in capturing the dynamic nature of music consumption. Nevertheless, traditional Transformer-based models, such as SASRec and BERT4Rec, while effective, encounter
Externí odkaz:
http://arxiv.org/abs/2409.04329
Autor:
Dagur, Deepak, Finardi, Alice Margherita, Polewczyk, Vincent, Petrov, Aleksandr Yu., Dolabella, Simone, Motti, Federico, Sharma, Hemanita, Dobovicnik, Edvard, Giugni, Andrea, Rossi, Giorgio, Fasolato, Claudia, Torelli, Piero, Vinai, Giovanni
Multiferroic heterostructures have gained in recent years a renewed role in spintronic applications due to their possibility in controlling the magnetic properties via interfacial coupling by exploiting the ferroelectric response to various external
Externí odkaz:
http://arxiv.org/abs/2408.14167
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 production environments with catalogues of millions of items: scaling
Externí odkaz:
http://arxiv.org/abs/2408.09992
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 computationally expensive and allow for scoring only a small number of docum
Externí odkaz:
http://arxiv.org/abs/2403.20222
Autor:
Petrov, Aleksandr, Macdonald, Craig
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. These models treat items as tokens and then utilise a score-
Externí odkaz:
http://arxiv.org/abs/2403.04875
Autor:
Petrov, Aleksandr V., Macdonald, Craig
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 behaviour. Current state-of-the-art Transformer-based models for sequential re
Externí odkaz:
http://arxiv.org/abs/2312.06165
The Coulomb drag is a many-body effect observed in proximized low-dimensional systems. It appears as emergence of voltage in one of them upon passage of bias current in another. The magnitude of drag voltage can be strongly affected by exchange of pl
Externí odkaz:
http://arxiv.org/abs/2312.05097
Autor:
Petrov, Aleksandr, Macdonald, Craig
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 negat
Externí odkaz:
http://arxiv.org/abs/2308.07192
Autor:
Petrov, Aleksandr V., Macdonald, Craig
Sequential recommendation is an important recommendation task that aims to predict the next item in a sequence. Recently, adaptations of language models, particularly Transformer-based models such as SASRec and BERT4Rec, have achieved state-of-the-ar
Externí odkaz:
http://arxiv.org/abs/2306.11114
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 datasets, such as MovieLens or Netflix, our dataset is based on the implic
Externí odkaz:
http://arxiv.org/abs/2209.00325