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.
Guiding the coarse levels of semantic IDs makes the fine levels learnable
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arXiv AI
5d ago
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.
Hugging Face Trending Papers
6d ago
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
Jun 2
LLMs Need Encoders for Semantic IDs Too
arXiv:2606. 00324v1 Announce Type: cross Abstract: Multimodal LLMs use dedicated encoders to bridge non-language modalities (vision encoders for images, depth models for audio codec tokens) because raw token embeddings alone cannot capture modality-specific structure.
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.
arXiv AI
Jun 2
Deep Interest Mining for Intent-Enriched Semantic IDs in Multimodal Generative Recommendation
arXiv:2604. 20861v3 Announce Type: replace-cross Abstract: Semantic IDs (SIDs) provide the discrete item vocabulary used by generative recommendation, but their quality depends on what item evidence is preserved before quantization.
arXiv AI
Sep 1
Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval
The paper introduces CHAP, a personalized generative retrieval framework that aligns query semantics with item representations through a hierarchical semantic alignment module and models user behavior using both discrete Semantic IDs and continuous representations. It also proposes a Residual Cascading Generation mechanism to reduce inference latency by limiting the Transformer decoder to a single pass. Experiments on multiple datasets and online A/B tests show that CHAP outperforms existing methods, demonstrating its practical value.
arXiv Machine Learning
Aug 11
Preserving Item Semantics for Free: Rethinking Token Initialization in LLM-Based Generative Recommendation
arXiv:2608. 07816v1 Announce Type: cross Abstract: Recent advances in generative recommendation (GR) leverage large language models (LLMs) as recommender backbones, enabling LLMs to directly generate recommendations conditioned on item-interaction histories.
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 15
Implicit Reasoning for Large Language Model-based Generative Recommendation
arXiv:2606. 14142v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge.
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
Jun 10
Reasoning over Semantic IDs Enhances Generative Recommendation
arXiv:2603. 23183v2 Announce Type: replace-cross Abstract: Recent advances in generative recommendation have leveraged pretrained LLMs by formulating sequential recommendation as autoregressive generation over a unified token space comprising language tokens and itemic identifiers, where each item is represented by a compact sequence of discrete tokens, namely Semantic IDs (SIDs).