# Semantic IDs for Joint Generative Search and Recommendation

arXiv: https://arxiv.org/abs/2508.10478 · HTML: https://mlsanity.com/paper/2508.10478/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2025.

## Built on by (most cited first, 14 of 14)

- [Masked Diffusion for Generative Recommendation](https://mlsanity.com/paper/2511.23021/) (2025)
- [Variable-Length Semantic IDs for Recommender Systems](https://mlsanity.com/paper/2602.16375/) (2026)
- [Understanding Semantic IDs: From Item Representation to Item Selection in Generative Recommendation](https://mlsanity.com/paper/2607.24995/) (2026)
- [Learning from Unreachable Rewards: Hint-Conditioned Reinforcement Learning for Generative Recommendation](https://mlsanity.com/paper/2608.11980/) (2026)
- [Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval](https://mlsanity.com/paper/2512.12458/) (2025)
- [One Model, Two Markets: Bid-Aware Generative Recommendation](https://mlsanity.com/paper/2603.22231/) (2026)
- [Aligning Recommendations with User Popularity Preferences](https://mlsanity.com/paper/2604.01036/) (2026)
- [FlashTrie: A GPU-Accelerated Constrained Beam Search for Generative Retrieval](https://mlsanity.com/paper/2607.10044/) (2026)
- [EchoRec: Multi-Item Prediction-Empowered Generative Recommendation via Cycle-Consistent Preference Alignment](https://mlsanity.com/paper/2608.14011/) (2026)
- [Dear Algo: A Precision-First Agentic Intent Layer for Unified Search and Recommendation](https://mlsanity.com/paper/2608.15877/) (2026)
- [SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation](https://mlsanity.com/paper/2608.18779/) (2026)
- [One Hierarchy, Two Systems: Semantic Product IDs for Discovery-Surface Ranking and Search-Page Query Reformulation](https://mlsanity.com/paper/2608.20640/) (2026)
- [Balancing Trial and Reorder: A Hybrid Sequential Transformer-GBDT Ranker for On-Demand Delivery](https://mlsanity.com/paper/2609.16407/) (2026)
- [From Interests to Semantic IDs: Retrieval-Grounded Credit Assignment for Generative Recommendation](https://mlsanity.com/paper/2609.29983/) (2026)

## Abstract (arXiv)

Generative models powered by Large Language Models (LLMs) are emerging as a unified solution for powering both recommendation and search tasks. A key design choice in these models is how to represent items, traditionally through unique identifiers (IDs) and more recently with Semantic IDs composed of discrete codes, obtained from embeddings. While task-specific embedding models can improve performance for individual tasks, they may not generalize well in a joint setting. In this paper, we explore how to construct Semantic IDs that perform well both in search and recommendation when using a unified model. We compare a range of strategies to construct Semantic IDs, looking into task-specific and cross-tasks approaches, and also whether each task should have its own semantic ID tokens in a joint search and recommendation generative model. Our results show that using a bi-encoder model fine-tuned on both search and recommendation tasks to obtain item embeddings, followed by the construction of a unified Semantic ID space provides an effective trade-off, enabling strong performance in both tasks. We hope these findings spark follow-up work on generalisable, semantically grounded ID schemes and inform the next wave of unified generative recommender architectures.