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Preference-Drift-Aware Subsequence Learning and Hierarchical Context Fusion for Long-Sequence Generative Recommendation — arXiv:2609.12556

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.12556
License (as archived): CC0-1.0
Archived: 2026-10-16T03:57:16.168Z
SHA-256 of the archived text: 072297a40889db9295ff1cf966d223c6aae6f4b9981ab42003177044d7d64fe6
Stable URL of this page: https://metraipolis.com/documento/dc_cc2b5145f778

How to cite

Preference-Drift-Aware Subsequence Learning and Hierarchical Context Fusion for Long-Sequence Generative Recommendation — arXiv:2609.12556 — arXiv (en), https://arxiv.org/abs/2609.12556 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cc2b5145f778 (Metraipolis archive dc_cc2b5145f778, sha256 072297a40889db9295ff1cf966d223c6aae6f4b9981ab42003177044d7d64fe6)

BibTeX

@misc{dccc2b5145f778,
  title = {Preference-Drift-Aware Subsequence Learning and Hierarchical Context Fusion for Long-Sequence Generative Recommendation — arXiv:2609.12556},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.12556},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cc2b5145f778; sha256 072297a40889db9295ff1cf966d223c6aae6f4b9981ab42003177044d7d64fe6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.12556. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Long-sequence generative recommendation methods autoregressively model the user's interaction sequence to generate the next-item representation. Existing methods generally fall into two categories: efficient full-sequence modeling and target-aware context retrieval. Our experiments reveal that as the sequence length increases, the former incurs steadily growing computational cost while its accuracy gains quickly saturate and even degrade due to noise; the latter, though shortening the input sequence, is susceptible to noise that is semantically consistent yet preference-inconsistent, as well as to incomplete contexts. Both paradigms ignore the dynamic changes of user preferences and the cross-subsequence dependencies when handling historical information, thereby limiting accuracy and efficiency. To address these issues, we propose a preference-drift-aware subsequence learning and hierarchical context fusion for long-sequence generative recommendation. Specifically, we learn differentiable soft subsequence boundaries using multidimensional preference-drift information and aggregate items within each subsequence into preference-coherent representations via linear attention with soft assignment weights, thereby circumventing the expense of full-sequence attention. A cross-attention mechanism is then employed to capture dependencies between recent interactions and relevant subsequence contexts, mitigating noise in learning recent-item representations. Finally, a gated fusion mechanism adaptively combines the recent-item representation with the global subsequence context, allowing the resulting target representation to encode both recent and long-term preferences. Extensive experiments demonstrate that our method consistently outperforms existing baselines in both recommendation accuracy and computational efficiency.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-14 in cs.IR).
Categorie arXiv: Information Retrieval (cs.IR).
Pagina del lavoro: https://arxiv.org/abs/2609.12556 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T03:44:07.299Z dalla pagina https://arxiv.org/catchup/cs.IR/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).