Hugging Face Trending Papers
PRQ-KMeans: Projection Residual Quantization for Semantic ID Tokenization
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arXiv Machine Learning
Aug 26
PRQ-KMeans: Projection Residual Quantization for Semantic ID Tokenization
arXiv:2608.24207v1 Announce Type: new Abstract: Semantic identifiers (SIDs) represent entities as hierarchical token sequences for generative retrieval and recommendation. Residual-quantization token...
arXiv AI
Sep 30
FineSID: Scalable and Efficient Semantic Identifier Learning for Generative Recommendation
FineSID introduces a new quantization framework for semantic identifier learning in generative recommendation systems. By replacing the traditional Top‑1 hard assignment with a soft, differentiable approach, it distributes gradient updates across all codewords, leading to balanced codebook optimization and reduced identifier collisions. Experiments on public benchmarks show that FineSID improves codebook utilization and recommendation accuracy without relying on complex initialization strategies.
arXiv Machine Learning
Aug 24
From a Static Multi-Level Small Semantic Codebook to a Dynamic Single-Level Large Semantic Codebook for Generative Recommendation
The paper proposes replacing a static multi‑level small semantic codebook with a dynamic single‑level large semantic codebook for generative recommendation. It introduces an exposure‑aware update mechanism and an offline evaluation framework, achieving significant improvements in recall, NDCG, decoding efficiency, and online consumption metrics on public datasets and production traffic.
arXiv AI
Jun 26
The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation
arXiv:2512. 10388v3 Announce Type: replace-cross Abstract: Conventional Sequential Recommender Systems (SRS) typically assign unique hash IDs (HID) to construct item embeddings, which mainly capture collaborative signals from historical user-item interactions.
arXiv AI
Sep 10
Exploring Bottom-Up Clustering for Creating Semantic IDs
The paper proposes a new algorithm for generating Semantic IDs that are both unique and preserve the structure of the original embedding space. By employing bottom‑up clustering, the method maintains local structure, leading to higher clustering quality. This improved structure enhances the utility of the Semantic IDs for downstream generative retrieval tasks.
arXiv Machine Learning
Sep 22
Guiding the coarse levels of semantic IDs makes the fine levels learnable
arXiv:2609.22227v1 Announce Type: cross Abstract: Generative retrieval represents each item by a short Semantic ID and casts recommendation as autoregressive generation of that sequence. Because the...