Metraipolis › archive › dc_e1db2f4bb239

Personalization Matters: Long-Horizon Conversation Agent with User-Centric Information in Online Shopping Interactions — arXiv:2610.11375

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.11375
License (as archived): CC0-1.0
Archived: 2026-10-09T05:46:54.371Z
SHA-256 of the archived text: e0943ccab4837e4bea24d59d7b37b06777c2a8996c481cb91301ee2915b63234
Stable URL of this page: https://metraipolis.com/documento/dc_e1db2f4bb239

How to cite

Personalization Matters: Long-Horizon Conversation Agent with User-Centric Information in Online Shopping Interactions — arXiv:2610.11375 — arXiv (en), https://arxiv.org/abs/2610.11375 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e1db2f4bb239 (Metraipolis archive dc_e1db2f4bb239, sha256 e0943ccab4837e4bea24d59d7b37b06777c2a8996c481cb91301ee2915b63234)

BibTeX

@misc{dce1db2f4bb239,
  title = {Personalization Matters: Long-Horizon Conversation Agent with User-Centric Information in Online Shopping Interactions — arXiv:2610.11375},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.11375},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e1db2f4bb239; sha256 e0943ccab4837e4bea24d59d7b37b06777c2a8996c481cb91301ee2915b63234},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.11375. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Personalized conversational shopping requires maintaining preference consistency over multi-turn interactions, where users reveal constraints gradually. Existing approaches often rely on static profiles and do not explicitly control long-horizon interaction behavior. We propose a multi-agent, multimodal Retrieval-Augmented Generation (RAG) framework that decomposes dialogue state tracking, recommendation retrieval, preference-aware reasoning, and response generation, while integrating product metadata, product reviews, image-derived descriptions, and user historical reviews. To evaluate interaction-level quality, we adopt a trajectory-level protocol with four dimensions: Global Preference Consistency, Cumulative Information Synthesis, Interaction Trajectory, and Tone Consistency. On an Amazon Reviews 2023 benchmark, retrieval-enabled variants outperform a no-RAG baseline on automatic trajectory metrics (average 4.82 vs. 3.74). In a small real-user study ($n{=}5$), the Full variant achieves the highest mean overall rating (4.60 vs. 2.20 for Baseline), providing exploratory evidence that role decomposition plus user-centric retrieval improves perceived personalization.\footnote{Code and dataset are available at: this https URL

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2610.11375 — 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 2026-10-09T05:46:44.087Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).