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Arxiv
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FEASE: Shallow AutoEncoding Recommender with Cold Start Handling via Side Features
- FEASE is an augmented EASE model that addresses user and item cold starts in recommendation systems
- It seamlessly integrates user and item side information to handle cold start issues
- FEASE leverages rich content signals for cold items and refines user representations in data-sparse environments
- Experimental results show improved recommendation accuracy and robustness compared to previous collaborative filtering approaches
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