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.
