Human-Centric Intelligent Systems (Jun 2024)

X-Model4Rec: An Extensible Recommender Model Based on the User’s Dynamic Taste Profile

  • Rogério Xavier de Azambuja,
  • A. Jorge Morais,
  • Vítor Filipe

DOI
https://doi.org/10.1007/s44230-024-00073-3
Journal volume & issue
Vol. 4, no. 3
pp. 344 – 362

Abstract

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Abstract Several approaches have been proposed to obtain successful models to solve complex next-item recommendation problem in non-prohibitive computational time, such as by using heuristics, designing architectures, and applying information filtering techniques. In the current technological scenario of artificial intelligence, sequential recommender systems have been gaining attention and they are a highly demanding research area, especially using deep learning in their development. Our research focuses on an efficient and practical model for managing sequential session-based recommendations of specific products for users using the wine and movie domains as case studies. Through an innovative recommender model called X-Model4Rec – eXtensible Model for Recommendation, we explore the user's dynamic taste profile using architectures with transformer and multi-head attention mechanisms to solve the next-item recommendation problem. The performance of the proposed model is compared to that of classical and baseline recommender models on two real-world datasets of wines and movies, and the results are better for most of the evaluation metrics.

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