衡量推荐中的策略化行为：用户调整其行为以塑造未来内容
Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content
AI总结：
本文通过定制音乐播放器的实验室实验与调查，验证用户会依据推荐算法机制和激励调整点赞、停留等行为以塑造未来推荐，表明策略化普遍且平台不可忽视。
摘要：
大多数现代推荐算法都是数据驱动的：它们通过观察用户的过去行为来生成个性化推荐。推荐中的一个常见假设是，用户与一条内容互动的方式（例如他们是否选择“点赞”它）反映的是内容本身，而不是生成该内容的算法。尽管这一假设很方便，但它未能捕捉到用户策略化：用户可能会通过让自己的行为适应推荐算法，试图塑造其未来推荐。在本研究中，我们通过开展一项实验室实验和调查来检验用户策略化。为了刻画策略化，我们采用了一个模型，在该模型中，策略性用户选择其互动行为时不仅基于内容，还基于其行为如何影响下游推荐。使用我们构建的一个定制音乐播放器，我们研究用户如何响应关于其推荐算法的不同信息，以及关于其行动如何影响下游结果的不同激励。我们在各项结果指标（包括参与者的停留时间和“点赞”使用）中发现了策略化的有力证据。例如，被告知算法主要关注“点赞”和“点踩”的参与者使用这些功能的频率，是被告知算法主要关注停留时间的参与者的1.9倍。对参与者行为的细致分析（例如针对我们的激励条件的反应）排除了实验者需求是这些趋势的主要驱动因素。此外，在实验后调查中，近一半参与者自我报告曾“在真实环境中”采取策略，其中一些人表示，他们会忽略自己真正喜欢的内容，以避免未来该内容被过度推荐。总之，我们的研究结果表明，用户策略化很常见，平台不能忽视其算法对用户行为的影响。
英文摘要：
Most modern recommendation algorithms are data-driven: they generate personalized recommendations by observing users' past behaviors. A common assumption in recommendation is that how a user interacts with a piece of content (e.g., whether they choose to "like" it) is a reflection of the content, but not of the algorithm that generated it. Although this assumption is convenient, it fails to capture user strategization: that users may attempt to shape their future recommendations by adapting their behavior to the recommendation algorithm. In this work, we test for user strategization by conducting a lab experiment and survey. To capture strategization, we adopt a model in which strategic users select their engagement behavior based not only on the content, but also on how their behavior affects downstream recommendations. Using a custom music player that we built, we study how users respond to different information about their recommendation algorithm as well as to different incentives about how their actions affect downstream outcomes. We find strong evidence of strategization across outcome metrics, including participants' dwell time and use of "likes." For example, participants who are told that the algorithm mainly pays attention to "likes" and "dislikes" use those functions 1.9x more than participants told that the algorithm mainly pays attention to dwell time. A close analysis of participant behavior (e.g., in response to our incentive conditions) rules out experimenter demand as the main driver of these trends. Further, in our post-experiment survey, nearly half of participants self-report strategizing "in the wild," with some stating that they ignore content they actually like to avoid over-recommendation of that content in the future. Together, our findings suggest that user strategization is common and that platforms cannot ignore the effect of their algorithms on user behavior.
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