AI 中文总结
该研究针对活动-出行行为领域推荐系统忽视用户福利的问题，提出福利导向推荐框架，通过正效用概率、遗憾最小化准则设计算法，经智能体模拟验证，为优化推荐系统提供了实用方案。
AI 中文摘要
主流推荐系统（RS）依赖多种启发式方法对备选方案进行排序，但通常缺乏对用户福利的原则性考量，即接受推荐是否会让用户比其他备选方案更优。在基于活动的出行行为中，这一问题尤为突出，因为无论最终是否满意，用户都会产生无法收回的成本，如精力、时间。因此，现有系统可能基于流行度或协同过滤推荐选项，但仍可能让用户比附近或自行选择的选项更差。我们通过引入一种用于活动推荐的福利导向框架来解决这一差距，该框架根据净效用评估建议，净效用定义为体验收益减去出行成本。具体而言，我们形式化了两个可操作的决策准则：正效用概率（PUP）仅在非负净效用的概率超过阈值时进行推荐；遗憾最小化（RM）仅在相对于用户最佳自然选择的预期遗憾低于容忍水平时进行推荐。为评估这些准则，我们开发了一个基于智能体的模拟系统，其中异构合成旅行者在具有现实出行成本、拥堵和行为反馈循环的空间环境中随时间与多个RS交互。该框架支持可控的反事实评估，并为设计将用户福利作为主要目标而非附带产物的RS提供了实用基础。
英文摘要
While mainstream recommender systems (RS) rely on diverse heuristics to rank alternatives, they generally lack a principled account of user welfare (i.e., whether accepting the recommendation will leave the user better off than other alternatives). The problem is particularly acute in activity-based travel behavior, where users incur costs they cannot recoup (i.e., energy, time) regardless of eventual satisfaction. As a result, existing systems may recommend options based on popularity or collaborative filtering, but may still leave users worse off than nearby or self-selected alternatives. We address this gap by introducing a welfare-oriented framework for activity recommendation that evaluates suggestions in terms of net utility, defined as experienced benefit minus travel costs. Specifically, we formalize two operational decision criteria: Positive Utility Probability (PUP) recommends only when the probability of non-negative net utility exceeds a threshold, while Regret Minimization (RM) recommends only when expected regret relative to the user's best organic alternative falls below a tolerance level. To evaluate these criteria, we develop an agent-based simulation in which heterogeneous synthetic travelers interact with multiple RS over time in a spatial environment with realistic travel costs, congestion, and behavioral feedback loops. This framework enables controlled counterfactual evaluations, and offers a practical foundation for designing RS that treat user welfare as a primary objective rather than an incidental byproduct.
