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    <abstract>Generative recommendation is an emerging paradigm that leverages the extensive knowledge of large language models by formulating recommendations into a text-to-text generation task. However, existing studies face two key limitations in (i) incorporating implicit item relationships and (ii) utilizing rich yet lengthy item information. To address these challenges, we propose a Generative Recommender via semantic-Aware Multi-granular late fusion (GRAM), introducing two synergistic innovations. First, we design semantic-to-lexical translation to encode implicit hierarchical and collaborative item relationships into the vocabulary space of LLMs. Second, we present multi-granular late fusion to integrate rich semantics efficiently with minimal information loss. It employs separate encoders for multi-granular prompts, delaying the fusion until the decoding stage. Experiments on four benchmark datasets show that GRAM outperforms eight state-of-the-art generative recommendation models, achieving significant improvements of 11.5-16.0% in Recall@5 and 5.3-13.6% in NDCG@5. The source code is available at https://github.com/skleee/GRAM.</abstract>
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%0 Conference Proceedings
%T GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion
%A Lee, Sunkyung
%A Choi, Minjin
%A Choi, Eunseong
%A Kim, Hye-young
%A Lee, Jongwuk
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F lee-etal-2025-gram
%X Generative recommendation is an emerging paradigm that leverages the extensive knowledge of large language models by formulating recommendations into a text-to-text generation task. However, existing studies face two key limitations in (i) incorporating implicit item relationships and (ii) utilizing rich yet lengthy item information. To address these challenges, we propose a Generative Recommender via semantic-Aware Multi-granular late fusion (GRAM), introducing two synergistic innovations. First, we design semantic-to-lexical translation to encode implicit hierarchical and collaborative item relationships into the vocabulary space of LLMs. Second, we present multi-granular late fusion to integrate rich semantics efficiently with minimal information loss. It employs separate encoders for multi-granular prompts, delaying the fusion until the decoding stage. Experiments on four benchmark datasets show that GRAM outperforms eight state-of-the-art generative recommendation models, achieving significant improvements of 11.5-16.0% in Recall@5 and 5.3-13.6% in NDCG@5. The source code is available at https://github.com/skleee/GRAM.
%R 10.18653/v1/2025.acl-long.1596
%U https://aclanthology.org/2025.acl-long.1596/
%U https://doi.org/10.18653/v1/2025.acl-long.1596
%P 33294-33312

Markdown (Informal)

[GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion](https://aclanthology.org/2025.acl-long.1596/) (Lee et al., ACL 2025)

• GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion (Lee et al., ACL 2025)

ACL

• Sunkyung Lee, Minjin Choi, Eunseong Choi, Hye-young Kim, and Jongwuk Lee. 2025. GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 33294–33312, Vienna, Austria. Association for Computational Linguistics.