Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content

arXiv:2405.05596v1 Announce Type: cross
Abstract: 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 …

abstract adapt algorithms arxiv behavior cs.cy cs.lg data data-driven future generate measuring modern personalized personalized recommendations recommendation recommendation algorithms recommendations stat.me type