Improving collaborative filtering with self-supervised GCNS and autoencoder base multimodal embeddings

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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Bibliographic Details
Main Author: Truong, Vinh Khai
Other Authors: Luo Siqiang
Format: Final Year Project (FYP)
Language:English
Published: Nanyang Technological University 2024
Subjects:
Computer and Information Science
Self-supervised GCN
Recommender system
Contrastive learning
Autoencoder
Multi-modal
Online Access:https://hdl.handle.net/10356/180714

Internet

https://hdl.handle.net/10356/180714

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