arXiv:2608. 09685v1 Announce Type: new Abstract: Autoregressive semantic ID recommenders are constrained by expensive beam-search decoding, which limits the practical length of item identifiers.
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arXiv:2607. 28659v1 Announce Type: new Abstract: Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains.
arXiv:2607. 25209v1 Announce Type: cross Abstract: Generative recommendation commonly represents items using fixed-length semantic identifiers (SIDs) constructed through clustering and quantization.
FineSID introduces a new quantization framework for semantic identifier learning in generative recommendation systems. By replacing the traditional Top‑1 hard assignment with a soft, differentiable approach, it distributes gradient updates across all codewords, leading to balanced codebook optimization and reduced identifier collisions. Experiments on public benchmarks show that FineSID improves codebook utilization and recommendation accuracy without relying on complex initialization strategies.
arXiv:2607. 25216v1 Announce Type: cross Abstract: Semantic ID-based generative recommendation tokenizes each item into a sequence of discrete semantic IDs and predicts the next item by generating semantic IDs.
arXiv:2605. 17779v2 Announce Type: replace Abstract: Generative recommendation reformulates recommendation as next-token prediction over discrete semantic identifiers (IDs).
arXiv:2608. 11980v1 Announce Type: cross Abstract: Semantic-ID generative recommenders represent each item as a short sequence of discrete semantic tokens and predict the next item by autoregressively generating this token sequence.