Representação Vetorial Consciente de Tempo para Recomendadores Incrementais
Resumo
With the growth of data volume, recommendation systems have evolved from static batch approaches to incremental models, allowing intelligent agents to treat recommendation as a dynamic decision process and learn continuously from user feedback. However, by prioritizing stages such as policy optimization and reward modeling, the current literature neglects important aspects of these systems, such as context representation and temporal consumption dynamics, factors intrinsically related to the continuous recommendation scenario. Building on this gap, this work introduces TAI2Vec, an item embedding generation model that integrates temporal variables to create latent representations adaptable to different consumption paces. Through an extensive experimental evaluation, we show that TAI2Vec outperforms traditional methods in recommendation tasks, especially in data-scarce scenarios, highlighting the ability of temporal information to enrich contextual learning. The source code is available at: https://github.com/UFSCar-LaSID/tai2vec.
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