GORACS selects small groups of fine-tuning examples via an optimal-transport and gradient-norm proxy objective, outperforming prior coreset methods for LLM-based recommendation.
A Survey on Data-Centric Recommender Systems
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abstract
Recommender systems (RSs) have become an essential tool for mitigating information overload in a range of real-world applications. Recent trends in RSs have revealed a major paradigm shift, moving the spotlight from model-centric innovations to data-centric efforts (e.g., improving data quality and quantity). This evolution has given rise to the concept of data-centric recommender systems (Data-Centric RSs), marking a significant development in the field. This survey provides the first systematic overview of Data-Centric RSs, covering 1) the foundational concepts of recommendation data and Data-Centric RSs; 2) three primary issues of recommendation data; 3) recent research developed to address these issues; and 4) several potential future directions of Data-Centric RSs.
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GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems
GORACS selects small groups of fine-tuning examples via an optimal-transport and gradient-norm proxy objective, outperforming prior coreset methods for LLM-based recommendation.