RSAttAE: An Information-Aware Attention-based Autoencoder Recommender System

Recommender systems play a crucial role in modern life, including information retrieval, the pharmaceutical industry, retail, and entertainment. The entertainment sector, in particular, attracts significant attention and generates substantial profits. This work proposes a new method for predicting unknown user-movie ratings to enhance customer satisfaction. To achieve this, we utilize the MovieLens 100K dataset. Our approach introduces an attention-based autoencoder to create meaningful representations and the XGBoost method for rating predictions. The results demonstrate that our proposal outperforms most of the existing state-of-the-art methods. Availability:this http URL

@article{taromi2025_2502.06705,
  title={ RSAttAE: An Information-Aware Attention-based Autoencoder Recommender System },
  author={ Amirhossein Dadashzadeh Taromi and Sina Heydari and Mohsen Hooshmand and Majid Ramezani },
  journal={arXiv preprint arXiv:2502.06705},
  year={ 2025 }
}