2 papers
cs.LG2026
Distill Globally, Adapt Locally: Reasoning Distillation and Product-Type Test-Time Training for Scalable Trade-Up Recommendation
Siliang Liu, Mohammad Ghasemi, Sapan Patel +1
Trade-up recommendation identifies higher-quality alternatives that preserve a customer's purchase intent while offering upgraded benefits. Large language models (LLMs) can reason…
cs.LG2024
Beauty Beyond Words: Explainable Beauty Product Recommendations Using Ingredient-Based Product Attributes
Siliang Liu, Rahul Suresh, Amin Banitalebi-Dehkordi
Accurate attribute extraction is critical for beauty product recommendations and building trust with customers. This remains an open problem, as existing solutions are often unreli…