RSAttAE: An Information-Aware Attention-based Autoencoder Recommender System
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866913685225603072 |
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| author | Taromi, Amirhossein Dadashzadeh Heydari, Sina Hooshmand, Mohsen Ramezani, Majid |
| author_facet | Taromi, Amirhossein Dadashzadeh Heydari, Sina Hooshmand, Mohsen Ramezani, Majid |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06705 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RSAttAE: An Information-Aware Attention-based Autoencoder Recommender System Taromi, Amirhossein Dadashzadeh Heydari, Sina Hooshmand, Mohsen Ramezani, Majid Machine Learning Information Retrieval 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 |
| title | RSAttAE: An Information-Aware Attention-based Autoencoder Recommender System |
| topic | Machine Learning Information Retrieval |
| url | https://arxiv.org/abs/2502.06705 |