Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content
arxiv(2024)
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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