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Think Globally, Generate Personally: Multi-View Thinking with Adaptive Generation for Generative Recommendation — arXiv:2611.02463

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.02463
License (as archived): CC0-1.0
Archived: 2026-11-03T10:32:42.587Z
SHA-256 of the archived text: 4fbb7760d498413ccb75399d7ded0bb807969b1afd3859141b8ffe64b702071c
Stable URL of this page: https://metraipolis.com/documento/dc_9264dbbdaa2e

How to cite

Think Globally, Generate Personally: Multi-View Thinking with Adaptive Generation for Generative Recommendation — arXiv:2611.02463 — arXiv (en), https://arxiv.org/abs/2611.02463 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_9264dbbdaa2e (Metraipolis archive dc_9264dbbdaa2e, sha256 4fbb7760d498413ccb75399d7ded0bb807969b1afd3859141b8ffe64b702071c)

BibTeX

@misc{dc9264dbbdaa2e,
  title = {Think Globally, Generate Personally: Multi-View Thinking with Adaptive Generation for Generative Recommendation — arXiv:2611.02463},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.02463},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_9264dbbdaa2e; sha256 4fbb7760d498413ccb75399d7ded0bb807969b1afd3859141b8ffe64b702071c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.02463. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Semantic-ID-based (SID) generative recommendation (GR) represents each item as a sequence of discrete tokens and predicts the next item through autoregressive SID generation. However, existing methods face two challenges. First, insufficient shared context modeling for user sequences: full-history and recent behaviors provide overlapping yet complementary evidence, but modeling them within a single sequence may dilute current cues, whereas processing them through separate streams may fail to coordinate them. Second, insufficient personalized adaptation in progressive SID decisions: although decoder states evolve with generated prefixes, existing models typically use a shared output-side transformation across recommendation instances, without explicitly selecting a transformation suited to the current user need. To address these challenges, we propose Multi-View Thinking with Adaptive Generation (MV-Thinker), a framework that Thinks Globally and Generates Personally. First, Multi-View Temporal Thinking uses view-specific thinking queries and readers over overlapping full-history and recent-history views, coordinating both sources into complementary user-conditioned contexts before SID generation. Second, Hierarchical Residual Adaptation organizes lightweight adapters into nested residual paths before a shared vocabulary projection, adaptively transforming context-bearing decoder states for SID decisions. Extensive experiments on four public datasets demonstrate that MV-Thinker consistently outperforms strong discriminative and generative recommendation baselines across multiple ranking metrics. The implementation code and datasets are available on: this https URL

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.02463 — 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 2026-11-03T10:29:48.559Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).