发表机构
Spotify USA; Spotify UK（Spotify美国; Spotify英国）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文指出智能体网络中推荐对象从人类转向AI智能体，提出委托频谱框架，并规划了智能体偏好建模与双受众优化等研究议程。
AI 中文摘要
二十年来，推荐系统的设计一直基于这样一个假设：人类直接消费每一条推荐——接收、理解并据此采取行动。由大语言模型驱动的AI智能体的出现对这一假设提出了挑战。在新兴的智能体网络[28]中，自主智能体越来越多地代表用户行事，例如浏览、比较、协商和执行交易，这引发了一个核心问题：推荐的接收者是谁？在这篇立场论文中，我们认为推荐范式正在经历一次分化。在可委托的情境中，例如日常购物、旅行和受限的交易任务，推荐的主要操作消费者正从人类转向智能体，这需要新的优化目标、交互协议和评估标准。在体验式情境中，例如娱乐、艺术和其他主观或高风险的抉择，人类仍然是相关性的最终评判者，尽管智能体可能通过预过滤和策展提供辅助。我们引入了一个委托频谱，该频谱沿偏好可明确性、结果可验证性和决策风险等因素刻画推荐情境，并概述了一个涵盖智能体偏好建模、双受众优化和新兴智能体注意力经济的研究议程。我们进一步讨论了这一转变对推荐系统设计和评估的影响。
英文摘要
For two decades, recommender systems have been designed under the assumption that a human directly consumes each recommendation: receiving, interpreting, and acting upon it. The emergence of AI agents powered by large language models challenges this assumption. In the emerging Agentic Web [ 28 ], autonomous agents increasingly act on behalf of users, e.g., browsing, comparing, negotiating, and executing transactions, raising a central question: who is the receiver of a recommendation? In this position paper, we argue that the recommendation paradigm is undergoing a bifurcation. In delegable contexts, such as routine purchases, travel, and constrained transactional tasks, the primary operational consumer of recommendations is shifting from the human to the agent, requiring new optimization objectives, interaction protocols, and evaluation criteria. In experiential contexts, such as entertainment, art, and other subjective or high-stakes choices, humans remain the final judge of relevance, though agents may assist through pre-filtering and curation. We introduce a delegation spectrum that characterizes recommendation contexts along factors such as preference specifiability, outcome verifiability, and decision stakes, and we outline a research agenda spanning agent preference modeling, dual-audience optimization, and the emerging agent attention economy. We further discuss the implications of this shift for the design and evaluation of recommender systems
