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.

By Mengdan Zhu, Yufan Zhao, Yao Zhao, Sophie Di, Tao Di, Yulan Yan, Sridhar Iyer, Liang Zhao
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.

By Xiangyi Chen, Zelun Wang, Xinyi Li, Yi-Ping Hsu, Jaewon Yang, Jiajing Xu