mlsanityData to 7 Oct 2026

Can Generative Recommendation Reach Cold Items? A Temporal Perspective on Semantic-ID Generation

arXiv 2607.21101 ↗First posted 23 Jul 2026cs.AIcited by 2

Not on the map. Why?

Abstract (arXiv)

Semantic-ID-based generative recommendation represents items as sequences of shared semantic tokens, enabling token recombination beyond isolated item IDs. However, closed-world recombination does not necessarily imply temporal open-token cold-start induction, where new items enter the item catalog with unseen atomic tokens or weakly supported SID paths. In this work, we revisit SID-based generative recommendation under an absolute-time temporal protocol that separates seen and unseen targets and diagnoses the cold item reachability at the token level. Through seen/unseen-hit analysis, coldness taxonomy, and oracle-prefix probing, we show that current SID-based models can occasionally reach future items supported by observed tokens and prefixes, but struggle with unseen atomic tokens and unsupported SID paths. We further explain this boundary by interpreting SID generation as hierarchical semantic bucketing: early tokens select coarse semantic regions, while later tokens refine item-specific paths. These findings show that SID generation is compositional but not fully open-ended, and suggest future directions in more independent SID spaces, scoring-based interfaces, and dynamic textual context.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 23 Jul 2026.

Builds on

17 of 29 references.

References · 17

  1. 2017Neural Discrete Representation Learningcited by 4,650
  2. 2018Self-Attentive Sequential Recommendationcited by 1,169
  3. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  4. 2016Wide & Deep Learning for Recommender Systemscited by 745
  5. 2022Autoregressive Image Generation using Residual Quantizationcited by 706
  6. 2015Image-based Recommendations on Styles and Substitutescited by 702
  7. 2022Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)cited by 396
  8. 2022Transformer Memory as a Differentiable Search Indexcited by 242
  9. 2024Learnable Item Tokenization for Generative Recommendationcited by 106
  10. 2020Exploring Data Splitting Strategies for the Evaluation of Recommendation Modelscited by 48
  11. 2020A Critical Study on Data Leakage in Recommender System Offline Evaluationcited by 38
  12. 2024CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendationcited by 26
5 more
  1. 2024Multimodality Invariant Learning for Multimedia-Based New Item Recommendationcited by 13
  2. 2026How Well Does Generative Recommendation Generalize?cited by 3
  3. 2026Cold-Starts in Generative Recommendation: A Reproducibility Studycited by 3
  4. 2025GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learningcited by 2
  5. 2025From Leakage to Fidelity: Reliable Benchmarking for Temporal Cascade Predictioncited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.