Many projects (both practical and academic) have designed algorithms to match users to content they will enjoy under the assumption that user's preferences and opinions do not change with the content they see. Evidence suggests that individuals' preferences are directly shaped by what content they see -- radicalization, rabbit holes, polarization, and boredom are all example phenomena of preferences affected by content. Polarization in particular can occur even in ecosystems with "mass media," where no personalization takes place, as recently explored in a natural model of preference dynamics by~\citet{hkazla2019geometric} and~\citet{gaitonde2021polarization}. If all users' preferences are drawn towards content they already like, or are repelled from content they already dislike, uniform consumption of media leads to a population of heterogeneous preferences converging towards only two poles. In this work, we explore whether some phenomenon akin to polarization occurs when users receive \emph{personalized} content recommendations. We use a similar model of preference dynamics, where an individual's preferences move towards content the consume and enjoy, and away from content they consume and dislike. We show that standard user reward maximization is an almost trivial goal in such an environment (a large class of simple algorithms will achieve only constant regret). A more interesting objective, then, is to understand under what conditions a recommendation algorithm can ensure stationarity of user's preferences. We show how to design a content recommendations which can achieve approximate stationarity, under mild conditions on the set of available content, when a user's preferences are known, and how one can learn enough about a user's preferences to implement such a strategy even when user preferences are initially unknown.
翻译:许多项目(实际的和学术的)都设计了使用户与所享受的内容相匹配的算法。 假设用户的偏好和观点不会随所看到的内容而改变。 有证据表明,个人偏好直接取决于他们所看到的内容 -- -- 激进化、兔子洞、两极分化和无聊都是受内容影响的偏好现象。 即使在生态系统中,“ 质量媒体” 也可能会发生极化, 而在“ 质量媒体” 中, 没有发生个人化现象。 最近通过“ citet{hkazla2019geology}” 和“citet{gaintde2021Polalization}” 来探讨的偏好性自然模式, 假设用户的偏好和观点不会随所见而改变。 如果所有用户的偏好是他们已经喜欢的内容, 或被他们已经不喜欢的内容所扭曲, 媒体的统一消费的偏好都直接决定了个人偏好, 媒体的偏好导致不同的偏好人群聚在一起, 只有两个极点。 在这项工作中, 我们探索, 当用户收到/emph{crial}内容的建议时, 那样的偏好地, 我们就可以知道, 当一个用户的偏好的偏好的偏好地, 当一个用户的偏好地, 当一个用户的排序可以学习到一个非常的排序, 当一个最接近于一个容易地, 当一个简单的 的排序的时候, 我们的排序, 我们的 在一个 在一个 的 的 在一个 在一个 的 的 的 的排序下, 我们的排序下, 我们的 的 的 的 的 的 的 的 。