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pro vyhledávání: '"Klenitskiy, Anton"'
The goal of modern sequential recommender systems is often formulated in terms of next-item prediction. In this paper, we explore the applicability of generative transformer-based models for the Top-K sequential recommendation task, where the goal is
Externí odkaz:
http://arxiv.org/abs/2409.17730
Using a single tool to build and compare recommender systems significantly reduces the time to market for new models. In addition, the comparison results when using such tools look more consistent. This is why many different tools and libraries for r
Externí odkaz:
http://arxiv.org/abs/2409.07272
Sequential recommender systems are an important and demanded area of research. Such systems aim to use the order of interactions in a user's history to predict future interactions. The premise is that the order of interactions and sequential patterns
Externí odkaz:
http://arxiv.org/abs/2408.12008
Autor:
Klenitskiy, Anton, Vasilev, Alexey
Recently sequential recommendations and next-item prediction task has become increasingly popular in the field of recommender systems. Currently, two state-of-the-art baselines are Transformer-based models SASRec and BERT4Rec. Over the past few years
Externí odkaz:
http://arxiv.org/abs/2309.07602