[Submitted on 18 Oct 2026]
Title:LightCRS: Lightweight Multi-Domain Conversational Recommendation System
View a PDF of the paper titled LightCRS: Lightweight Multi-Domain Conversational Recommendation System, by He Ma and 3 other authors
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Abstract: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.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.26948 [cs.AI]
(or arXiv:2610.26948v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.26948
arXiv-issued DOI via DataCite
Submission history
From: He Ma [view email] [v1] Sun, 18 Oct 2026 07:11:04 UTC (1,912 KB)
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