Hugging Face Trending Papers
PRQ-KMeans: Projection Residual Quantization for Semantic ID Tokenization
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arXiv Machine Learning
1d ago
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 Machine Learning
3d ago
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
1d ago
Tlow: Flow-based Item Tokenizer for Recommendation
The paper introduces Tlow, a flow-based item tokenizer that transforms raw semantic embeddings into a latent space following a standard normal distribution, enabling independent tokenization and simplifying distributional complexity. Tlow incorporates codebook guidance to align token embeddings with the codebook space, producing semantically clear token IDs. Experiments on four public datasets and an online multi‑modal retrieval task on WeChat show that Tlow improves recommendation performance and increases user click‑through rates by over 10%.
arXiv AI
Jul 29
VaLiDRec: Variable-Length LLM-Aligned Semantic IDs for Generative Recommendation
arXiv:2607. 25209v1 Announce Type: cross Abstract: Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization.
arXiv AI
Jul 29
TopoGR: Revealing and Preserving Latent Structure of Semantic ID in Generative Recommendation
arXiv:2607. 25216v1 Announce Type: cross Abstract: Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs.