AI 中文总结
本研究通过合成购物智能体压力测试，证明商业激励可通过选择性提问影响推荐结果，而无需进入排序算法，并量化了其效果与风险。
AI 中文摘要
商业激励不必进入最终排序算法即可影响购物助手的推荐：它反而可能影响助手询问哪个偏好问题。我们在一个刻意缩小的合成环境中使这一区别在实验上可观察。每个任务包含两个产品、三个已验证的数值属性、一个价格上限和一个私有的固定偏好向量。一个诚实的模拟用户回答一个成对问题。一个独立的推荐器接收产品和该答案，但不接收赞助分配。我们对比了中性问题、软性商业指令和明确对抗性指令（要求询问赞助商的优势而忽略竞争对手的优势）。在40个保留的赞助分配案例（20个不同的目录偏好上下文）中，软性指令未改变任何选择。对于一种语言模型推荐器，针对性指令将赞助选择提高了0.30，并将平均合成效用相对于中性提问降低了0.0547（95%上下文自助法区间[-0.0828, -0.0291]）。一个固定的贝叶斯推荐器显示出类似效果；第二个模型在所有120个冻结的问题-答案输入上做出相同选择。一个终端答案一致性评判器将所有20个采样的针对性答案评为一致，尽管其中五个的合成遗憾高于0.05；一个单独的问题覆盖维度标记了它们单侧引导的问题。一个稳健的部分偏好证书在规定的合成效用下仍然有效，但仅认证了40个针对性案例中的16个，并且并不比直接询问中性问题更好。这些结果既未确立广告激励下的典型行为，也未确立对实际消费者的影响。
英文摘要
A commercial incentive need not enter the final ranking algorithm to affect a shopping assistant's recommendation: it may instead influence which preference question the assistant asks. We make this distinction experimentally observable in a deliberately small, synthetic setting. Each task has two products, three verified numerical attributes, a price limit, and a private fixed preference vector. An honest simulated user answers one pairwise question. A separate recommender receives the products and this answer but not the sponsorship assignment. We contrast a neutral question, a soft commercial instruction, and an explicitly adversarial instruction to ask about the sponsor's advantage while omitting the rival's advantage. Across 40 held-out sponsorship-assignment cases (20 distinct catalog-preference contexts), the soft instruction changes no selections. The targeted instruction raises sponsored selection by 0.30 and reduces mean synthetic utility by 0.0547 relative to neutral questioning (95% context-bootstrap interval [-0.0828, -0.0291]) for one language-model recommender. A fixed Bayesian recommender shows a similar effect; a second model makes the same choices on all 120 frozen question-answer inputs. A terminal-answer consistency judge rates all 20 sampled targeted answers consistent, although five have synthetic regret above 0.05; a separate question-coverage dimension flags their one-sided elicitation. A robust partial-preference certificate remains valid under the stipulated synthetic utility but certifies only 16 of 40 targeted cases and is not better than asking a neutral question directly. These results establish neither typical behavior under advertising incentives nor effects on actual consumers.
Comments7 pages, 1 table; synthetic data and reproduction code in ancillary files; substantial AI-tool use disclosed in the manuscript
