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Cold-Start Recommendation
Learning Multi-Aspect Item Palette: A Semantic Tokenization Framework for Generative Recommendation
Published:9/11/2024
Generative Recommendation SystemsMulti-Aspect Semantic TokenizationText-Based Reconstruction TasksLong-Tailed Recommendation IssuesCold-Start Recommendation
This paper introduces LAMIA, a novel multiaspect semantic tokenization framework that enhances generative recommendation systems. Unlike traditional methods, it learns independent embeddings capturing multiple facets of items, significantly improving recommendation accuracy for
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Knowledge-aware Graph Neural Networks with Label Smoothness
Regularization for Recommender Systems
Published:5/11/2019
Knowledge Graph EmbeddingGNN-based Recommender SystemsLabel Smoothness RegularizationCold-Start RecommendationKnowledge-Aware Recommendation Methods
KGNNLS models userspecific KG relations with label smoothness regularization, enabling endtoend training for better personalized recommendations, notably improving coldstart performance and scalability, outperforming stateoftheart baselines across datasets.
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