Pairwise Ranking Outperforms Single-Action RL for Offline Explanation Selection: A Practical Lesson
Industrial explainable-recommendation systems built on LLMs incur a substantial serving cost: each request triggers an LLM generation, with latency in the hundreds of milliseconds and cost that scales linearly with traffic. We separate generation from selection: explanations are produced ahead of time as a frozen candidate pool (six prompt styles, two commodity LLMs), and a small CPU-resident selector picks one at request time. The stack needs no GPU and returns in under 100 ms. Our primary benchmark is a 2,958-pair XRec Google Local subset, evaluating six offline-pool selectors (LambdaRank, PPO, GRPO, DPO, teacher-student distillation) and three KG-path selectors (random walks, edge-disj...
| Authors | Tanay Chowdhury, Saeideh Shahrokh Esfahani |
|---|---|
| Primary category | cs.AI |
| Published | 19 August 2026 |
| Last updated at source | 20 September 2026 |
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