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arXiv AI By Aleksei Medvedev, Alejandro Ariza-Casabona, Steven Derby, Gonzalo Fiz Pontiveros, Xinyang Shao, Florian Spiess

Neither Black nor White: Balancing Semantic and Collaborative Signals with Graph-Informed Semantic IDs (GrIS)

Read the original on arXiv AI →

The paper introduces Graph-Informed Semantic IDs (GrIS), a framework that reframes Semantic ID construction as a recursive clustering problem on a graph combining semantic content and collaborative signals. GrIS generalises previous methods like RQ-VAE and RQ-KMeans by allowing explicit graph construction and hierarchical partitioning, and presents two implementations: RecDMoN and RQ-GAE. Experiments on real-world datasets show that GrIS outperforms collaborative-filtering aware state‑of‑the‑art models, achieving up to a 52% increase in Hit@10.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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