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About A Deep Learning Based Trust
This document proposes a deep learning-based recommender system that utilizes trust relationships and tag information to address data sparsity problems. It uses sparse autoencoders to extract latent features from user-user trust matrices and user-tag matrices, which have lower dimensions than the original data. These latent features are then used to calculate similarities between users and make recommendations. The proposed method is shown to outperform other recommender systems on benchmark datasets by tackling sparsity issues and reducing computational complexity through the use of deep learning to model supplementary information.
Detailed Information
| Author: | ['fani'] |
|---|---|
| Publication Year: | 2024 |
| Pages: | 22 |
| Language: | English |
| Format: | |
| Price: | FREE |
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