DIGER makes semantic IDs in generative recommendation differentiable via Gumbel noise and decay schedules, yielding consistent gains on public datasets by aligning indexing and recommendation losses.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.IR 3years
2026 3representative citing papers
Generative retrieval beats dense retrieval and BM25 on the LIMIT dataset but degrades with hard negatives due to identifier ambiguity during decoding.
SA²CRQ uses sequential adaptive residual quantization based on path entropy plus anchored curriculum regularization from head items to improve both efficiency and cold-start performance in generative retrieval.
citing papers explorer
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Differentiable Semantic ID for Generative Recommendation
DIGER makes semantic IDs in generative recommendation differentiable via Gumbel noise and decay schedules, yielding consistent gains on public datasets by aligning indexing and recommendation losses.
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Generative Retrieval Overcomes Limitations of Dense Retrieval but Struggles with Identifier Ambiguity
Generative retrieval beats dense retrieval and BM25 on the LIMIT dataset but degrades with hard negatives due to identifier ambiguity during decoding.
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Towards Efficient and Generalizable Retrieval: Adaptive Semantic Quantization and Residual Knowledge Transfer
SA²CRQ uses sequential adaptive residual quantization based on path entropy plus anchored curriculum regularization from head items to improve both efficiency and cold-start performance in generative retrieval.