Improving collaborative filtering with self-supervised GCNS and autoencoder base multimodal embeddings
Recommender systems play a crucial role in enhancing user experience by delivering personalized suggestions across diverse domains. Effective representation learning is vital in these systems, as high-quality embeddings are key to accurate recommendations, as evidenced by various studies. However, c...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/180714 |