2 papers
cs.IR2026
Auditing Semantic Gains in Sequential Recommendation: A Lightweight Recovery Test
Kong Wang, Zhongke He, Xiang Chen +4
Recent semantic and generative-retrieval recommenders report substantial improvements over ID-only sequential baselines, but it remains unclear whether these gains arise from langu…
cs.LG2026
Efficient Clustering with Quality Guardrails for LLM-based Recommender Systems at Industry Scale
Longshaokan Wang, Wai Tsang Keung, Punit Ghodasara +3
LLMs can be prohibitively expensive and slow to run at scale, especially for applications that invoke an LLM per sample over millions of inputs. A natural way to scale is to cluste…