arXiv AI
Jul 29
Understanding Semantic IDs: From Item Representation to Item Selection in Generative Recommendation
arXiv:2607. 24995v1 Announce Type: new Abstract: Semantic IDs (SIDs) are now a central component of generative recommendation.
Off-Policy Evaluation for Semantic ID Recommenders: Does the Model's Own Code Hierarchy Help?
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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...
arXiv AI
Jun 8
Understanding Generative Recommendation with Semantic IDs from a Model-scaling View
arXiv:2509. 25522v3 Announce Type: replace Abstract: Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recommendation (GR), which tries to unify rich item semantics and collaborative filtering signals.
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.
Hugging Face Trending Papers
Aug 19
SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation
SIDScope is a diagnostic tool that evaluates Semantic-ID mappings used between item tokenizers and generative recommenders. It normalizes artifacts, verifies provenance, profiles mapping structure, compares revisions, and tracks path-to-item outcomes in generated traces. Using data from Amazon and Yelp, SIDScope shows that interface health depends on multiple signals, revealing gaps in prefix alignment, trace accounting, and refresh handling that affect model reuse.
arXiv AI
Aug 20
SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation
SIDScope is a diagnostic tool that evaluates Semantic‑ID interfaces used in generative recommendation systems. It normalizes item‑to‑code artifacts, verifies provenance, profiles mapping structure, and compares revisions while tracking path‑to‑item outcomes in generated traces. Using nine tokenizer exports from Amazon and Yelp data, SIDScope shows that interface health depends on multiple signals and reveals gaps in prefix alignment, trace accounting, and mapping refresh effects.
arXiv AI
Sep 25
From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation
The paper proposes a retrieval‑grounded credit‑assignment method for generative recommenders that use Semantic IDs (SIDs). By structuring each generated trace into a history summary, a set of interest hypotheses, and a final SID, and then verifying each hypothesis with a frozen retriever, the method assigns reward at the hypothesis level rather than only at the final SID. Experiments on Amazon Reviews datasets show consistent improvements in SID recommendation, and an oracle analysis on Video Games data demonstrates that selecting target‑relevant queries among generated interests boosts recall and ranking.
arXiv AI
Jul 24
Can Generative Recommendation Reach Cold Items? A Temporal Perspective on Semantic-ID Generation
arXiv:2607. 21101v1 Announce Type: new Abstract: Semantic-ID-based generative recommendation represents items as sequences of shared semantic tokens, enabling token recombination beyond isolated item IDs.
arXiv AI
Aug 24
Difficulty-Aware Semantic-ID Optimization for Generative Recommendation
The paper introduces Difficulty‑Aware Semantic‑ID Optimization (DASO), a post‑training method for generative recommendation that improves tree‑structured item ranking. DASO profiles rollout groups by prefix‑match depth, reallocates a portion of candidates to prefix‑guided completions, and uses a SID‑prefix reward with an auxiliary SFT anchor to address target‑missing failures. On public benchmarks, DASO outperforms MiniOneRec‑style GRPO on 11 of 12 metrics and achieves the best results on 9 of 12 metrics, also improving level‑wise recall on an internal recommendation task.
arXiv Machine Learning
Sep 4
EPIC: Explicit Posterior Item Conditioning for Semantic ID Diffusion Recommendation
EPIC: Explicit Posterior Item Conditioning for Semantic ID Diffusion Recommendation proposes a method that adds explicit item-level competition into the denoising process of Semantic ID (SID) generative recommendation. The approach builds a personalized posterior over feasible candidate items based on the current generation context and the user's recent interactions, then projects this distribution back to unresolved SID positions to guide subsequent token decisions. Experiments on four Amazon benchmarks show consistent improvements over strong baselines, with diagnostic analyses indicating that the gains stem from personalized transition evidence that preserves promising item hypotheses during denoising.
Hugging Face Trending Papers
Sep 24
From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation
The paper introduces a retrieval‑grounded credit‑assignment method for generative recommenders that use Semantic IDs (SIDs). By structuring each autoregressive trace into a history summary, a set of interest hypotheses, and a final SID, a frozen retriever verifies each hypothesis as a catalog query. Rewards are assigned at the hypothesis level when any query retrieves the target within the top‑K, allowing distinct updates for rollouts that share the same SID reward and improving SID recommendation performance on Amazon Reviews datasets.
arXiv AI
Aug 18
SAPO: Step-Aligned Policy Optimization for Reasoning-Based Generative Recommendation
arXiv:2605. 17648v2 Announce Type: replace Abstract: Generative recommendation treats next-item prediction as autoregressive item-identifier generation.