Semantic IDs for Joint Generative Search and Recommendation
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.
68a582c138160bfd39426a64
Gustavo Penha
68a582c138160bfd39426a65
Edoardo D'Amico
68a582c138160bfd39426a66
Marco De Nadai
claimed_verified
2025-08-20T08:50:39.370Z
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/avatars/44a34405e4821fa9047cfa635e198f61.svg
Marco De Nadai
user
marcodena
68a582c138160bfd39426a67
Enrico Palumbo
68a582c138160bfd39426a68
Alexandre Tamborrino
68a582c138160bfd39426a69
Ali Vardasbi
68a582c138160bfd39426a6a
Max Lefarov
68a582c138160bfd39426a6b
Shawn Lin
68a582c138160bfd39426a6c
Timothy Heath
68a582c138160bfd39426a6d
Francesco Fabbri
claimed_verified
2025-08-20T08:50:37.227Z
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/avatars/a420d0e2c77a482eb5022deeccb2ebf5.svg
Francesco Fabbri
user
frafabbri
68a582c138160bfd39426a6e
Hugues Bouchard
Semantic IDs for Joint Generative Search and Recommendation
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.
A bi-encoder model fine-tuned on both search and recommendation tasks provides effective Semantic IDs for a unified generative recommender system.
Large Language Models (LLMs) Semantic IDs embeddings bi-encoder model task-specific embedding models semantic ID tokens unified Semantic ID space