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Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation — arXiv:2608.21243

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.21243
License (as archived): CC0-1.0
Archived: 2026-10-18T10:48:05.787Z
SHA-256 of the archived text: 814adf90f31bb181786876fe60702156e4d1e6f1d5c08618988700acbf857f2d
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How to cite

Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation — arXiv:2608.21243 — arXiv (en), https://arxiv.org/abs/2608.21243 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c5bc4fa48fc8 (Metraipolis archive dc_c5bc4fa48fc8, sha256 814adf90f31bb181786876fe60702156e4d1e6f1d5c08618988700acbf857f2d)

BibTeX

@misc{dcc5bc4fa48fc8,
  title = {Adapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation — arXiv:2608.21243},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.21243},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c5bc4fa48fc8; sha256 814adf90f31bb181786876fe60702156e4d1e6f1d5c08618988700acbf857f2d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.21243. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Sequential recommendation predicts the next item from a user's interaction history, but not every interaction is equally informative. Real logs combine persistent preferences with temporary needs, exploration, and incidental behavior, so some interactions can distort history representations or provide unreliable supervision. Existing denoising methods judge such interactions mainly from co-occurrence, order, or model predictions, without explicit evidence from relations between items. Knowledge graphs (KGs) offer this evidence, but item popularity, graph degree, uneven coverage, and widely shared entities can inflate connectivity and bias reliability estimates. Here we present AdaptedKG, which derives calibrated KG evidence for each training example without adding graph representations to the recommendation model. It first compares the observed context with structurally matched alternatives to identify relational paths that are unusually prominent and uses them to build a local KG view. It then compares each interaction with structurally matched reference items to calibrate its support within that view. The resulting retention coefficients gate historical representations and reweight target losses. All sample-specific scores are computed offline using training interactions and a fixed KG, so the backbone remains unchanged and no KG access is required at inference. Experiments show gains with a standard sequential recommender and multiple behavior-denoising sequential recommenders.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-24 in cs.IR).
Categorie arXiv: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.21243 — 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 2026-10-18T10:47:39.577Z dalla pagina https://arxiv.org/catchup/cs.IR/2026-08-24?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).