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arXiv AI By Jinming Nian, Jyun-Yu Jiang, Henry Tsai, Yi Fang, Zhirong Liang

Evolving Semantic Indexes for Generative Retrieval

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arXiv:2606. 31984v1 Announce Type: cross Abstract: Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the final re-ranking step disproportionately shapes user engagement and downstream performance, particularly for carousel and grid display formats.

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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.

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