Semantic IDs for Joint Generative Search and Recommendation
Authors: name: Gustavo Penha, name: Edoardo D'Amico, name: Marco De Nadai, name: Enrico Palumbo, name: Alexandre Tamborrino, name: Ali Vardasbi, name: Max Lefarov, name: Shawn Lin, name: Timothy Heath, name: Francesco Fabbri, name: Hugues Bouchard

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.