LightCRS boosts multi-domain conversational recommendation accuracy and speed
LightCRS is a new lightweight framework that unifies multimodal conversational recommendation across many product categories. The system pre‑computes product embeddings offline with frozen vision‑language encoders, then uses a compact 5.3 M‑parameter query‑conditioned cross‑attention module that is shared across all domains. An inter‑candidate Transformer ranks candidates jointly during the…
Key points
- LightCRS uses a 5.3 M‑parameter query‑conditioned module shared across 25 product domains
- It raises HR@1 by 35.4 % versus the strongest baseline and cuts per‑query latency by 266×
- Joint multi‑domain training adds another 15.2 % HR@1 gain over single‑domain models
The authors built a 25‑domain benchmark called MM‑R2A‑25, pairing natural Reddit recommendation‑seeking conversations with Amazon product listings. On this test set LightCRS achieves state‑of‑the‑art results, improving HR@1 by 35.4 % over the strongest baseline and cutting per‑query latency by 266 times. Training the model jointly on all domains also adds a 15.2 % HR@1 gain compared with separately trained single‑domain models.
These findings suggest that a single shared module can replace costly per‑domain vision‑language models while delivering higher accuracy and far lower response times, a potential win for large‑scale e‑commerce recommendation services.
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