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LightCRS: Lightweight Multi-Domain Conversational Recommendation System — arXiv:2610.26948

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.26948
License (as archived): CC0-1.0
Archived: 2026-10-21T07:05:43.865Z
SHA-256 of the archived text: 7a0937719f4c2b302910a2e4c4f84fdf6ff88defee1ebd10908eff0ed974a9d6
Stable URL of this page: https://metraipolis.com/documento/dc_30dabc42e3da

How to cite

LightCRS: Lightweight Multi-Domain Conversational Recommendation System — arXiv:2610.26948 — arXiv (en), https://arxiv.org/abs/2610.26948 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_30dabc42e3da (Metraipolis archive dc_30dabc42e3da, sha256 7a0937719f4c2b302910a2e4c4f84fdf6ff88defee1ebd10908eff0ed974a9d6)

BibTeX

@misc{dc30dabc42e3da,
  title = {LightCRS: Lightweight Multi-Domain Conversational Recommendation System — arXiv:2610.26948},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.26948},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_30dabc42e3da; sha256 7a0937719f4c2b302910a2e4c4f84fdf6ff88defee1ebd10908eff0ed974a9d6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.26948. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Conversational recommender systems have recently adopted vision-language models to match user dialogue intents with multimodal product information. However, extending these methods to many product domains faces a dilemma: deploying a separately adapted VLM per domain scales cost linearly with the number of domains and requires routing each dialogue to the right model, while naively merging all domains into a single VLM degrades accuracy because product characteristics vary widely across categories. We propose LightCRS, a lightweight, query-conditioned framework for unified multi-domain multimodal conversational recommendation. LightCRS precomputes multimodal product representations once offline with frozen encoders. Online, a compact 5.3M-parameter module fuses this evidence for each dialogue via query-conditioned cross-attention, whose parameters are shared across all domains, and compares candidates jointly with an inter-candidate Transformer. To enable rigorous evaluation, we construct MM-R2A-25, a 25-domain benchmark pairing natural Reddit recommendation-seeking conversations with Amazon product listings. LightCRS achieves state-of-the-art average HR@1, NDCG@5, and MRR, improving HR@1 by 35.4\% over the strongest baseline while reducing per-query latency by 266 times. Joint multi-domain training itself improves HR@1 by 15.2\% over separately trained single-domain models.

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/2610.26948 — 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 2026-10-21T07:05:43.371Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).