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: github.com/ComputationIASBS/RecommSys
@article{arxiv.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}
}