Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs
Semantic ID (SID)-based recommendation is a promising paradigm for scaling sequential recommender systems, but existing methods largely follow a semantic-centric pipeline: item embeddings are learned from foundation models and discretized using generic quantization schemes. This design is misaligned with generative recommendation objectives: semantic embeddings are weakly coupled with collaborative prediction, and generic quantization is inefficient at reducing sequential uncertainty for autoregressive modeling. To address these, we propose ReSID, a recommendation-native, principled SID framework that rethinks representation learning and quantization from the perspective of information preservation and sequential predictability, without relying on LLMs. ReSID consists of two components: (i) Field-Aware Masked Auto-Encoding (FAMAE), which learns predictive-sufficient item representations from structured features, and (ii) Globally Aligned Orthogonal Quantization (GAOQ), which produces compact and predictable SID sequences by jointly reducing semantic ambiguity and prefix-conditional uncertainty. Theoretical analysis and extensive experiments across ten datasets show the effectiveness of ReSID. ReSID consistently outperforms strong sequential and SID-based generative baselines by an average of over 10%, while reducing tokenization cost by up to 122x. Code is available at https://github.com/FuCongResearchSquad/ReSID.
698228299c2f139721ec350e
Yu Liang
claimed_verified
2026-02-04T19:03:59.622Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/694e1505a624c194a198a20c/ZS98Y8FcGS3CQOH2wnqkd.jpeg
Yu Liang
Ricardoly
user
Ricardoly
698228299c2f139721ec350f
Zhongjin Zhang
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Yuxuan Zhu
claimed_verified
2026-02-04T12:31:16.557Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/RtOK_qV71NM87A67XFJCN.png
Yuxuan Zhu
Ethan7
user
Ethan7
698228299c2f139721ec3511
Kerui Zhang
698228299c2f139721ec3512
Zhiluohan Guo
698228299c2f139721ec3513
Wenhang Zhou
698228299c2f139721ec3514
Zonqi Yang
698228299c2f139721ec3515
Kangle Wu
698228299c2f139721ec3516
Yabo Ni
698228299c2f139721ec3517
Anxiang Zeng
698228299c2f139721ec3518
Cong Fu
698228299c2f139721ec3519
Jianxin Wang
698228299c2f139721ec351a
Jiazhi Xia
Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMs
Semantic ID (SID)-based recommendation is a promising paradigm for scaling sequential recommender systems, but existing methods largely follow a semantic-centric pipeline: item embeddings are learned from foundation models and discretized using generic quantization schemes. This design is misaligned with generative recommendation objectives: semantic embeddings are weakly coupled with collaborative prediction, and generic quantization is inefficient at reducing sequential uncertainty for autoregressive modeling. To address these, we propose ReSID, a recommendation-native, principled SID framework that rethinks representation learning and quantization from the perspective of information preservation and sequential predictability, without relying on LLMs. ReSID consists of two components: (i) Field-Aware Masked Auto-Encoding (FAMAE), which learns predictive-sufficient item representations from structured features, and (ii) Globally Aligned Orthogonal Quantization (GAOQ), which produces compact and predictable SID sequences by jointly reducing semantic ambiguity and prefix-conditional uncertainty. Theoretical analysis and extensive experiments across ten datasets show the effectiveness of ReSID. ReSID consistently outperforms strong sequential and SID-based generative baselines by an average of over 10%, while reducing tokenization cost by up to 122x. Code is available at https://github.com/FuCongResearchSquad/ReSID.
ReSID presents a novel recommendation-native framework that improves sequential recommendation by learning predictive item representations and optimizing quantization for information preservation and sequential predictability.
semantic ID sequential recommender systems generative recommendation foundation models quantization masked auto-encoding orthogonal quantization information preservation sequential predictability collaborative prediction autoregressive modeling