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arXiv · 2502.06705

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

Amirhossein Dadashzadeh Taromi·Sina Heydari·Mohsen Hooshmand·Majid Ramezani
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Abstract

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

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Amirhossein Dadashzadeh Taromi, Sina Heydari, Mohsen Hooshmand, Majid Ramezani. 2025-02-10. RSAttAE: An Information-Aware Attention-based Autoencoder Recommender System. https://arxiv.org/abs/2502.06705

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