On social networks, algorithmic personalization drives users into filter bubbles where they rarely see content that deviates from their interests. We present a model for content curation and personalization that avoids filter bubbles, along with algorithmic guarantees and nearly matching lower bounds. In our model, the platform interacts with $n$ users over $T$ timesteps, choosing content for each user from $k$ categories. The platform receives stochastic rewards as in a multi-arm bandit. To avoid filter bubbles, we draw on the intuition that if some users are shown some category of content, then all users should see at least a small amount of that content. We first analyze a naive formalization of this intuition and show it has unintended consequences: it leads to ``tyranny of the majority'' with the burden of diversification borne disproportionately by those with minority interests. This leads us to our model which distributes this burden more equitably. We require that the probability any user is shown a particular type of content is at least $\gamma$ times the average probability all users are shown that type of content. Full personalization corresponds to $\gamma = 0$ and complete homogenization corresponds to $\gamma = 1$; hence, $\gamma$ encodes a hard cap on the level of personalization. We also analyze additional formulations where the platform can exceed its cap but pays a penalty proportional to its constraint violation. We provide algorithmic guarantees for optimizing recommendations subject to these constraints. These include nearly matching upper and lower bounds for the entire range of $\gamma \in [0,1]$ showing that the reward of a multi-agent variant of UCB is nearly optimal. Using real-world preference data, we empirically verify that under our model, users share the burden of diversification with only minor utility loss under our constraints.

翻译：在社交网络中，算法个性化使用户陷入信息茧房，鲜少接触到与其兴趣相悖的内容。我们提出了一种避免信息茧房的内容策展与个性化模型，同时给出了算法保证及近乎匹配的下界。在该模型中，平台与$n$名用户进行$T$轮交互，为每位用户从$k$个类别中选择内容。平台获得类似于多臂老虎机中的随机奖励。为避免信息茧房，我们基于如下直觉：若某些用户接触到某类内容，则所有用户都应至少看到该内容的少量片段。我们首先分析了该直觉的朴素形式化，并发现其存在意外后果：它导致"多数人暴政"，使得多样化的负担不成比例地由少数群体承担。这促使我们设计出更公平分配该负担的模型。我们要求任意用户看到特定类型内容的概率至少为$\gamma$倍的所有用户看到该类型内容的平均概率。完全个性化对应$\gamma = 0$，完全同质化对应$\gamma = 1$；因此$\gamma$编码了对个性化程度的硬性上限。我们还分析了其他形式化方案，其中平台可超过上限但需支付与违规比例成正比的罚金。我们为在这些约束下优化推荐提供了算法保证，包括针对$\gamma \in [0,1]$整个区间给出近乎匹配的上界与下界，证明多智能体UCB变体的奖励近乎最优。利用真实世界偏好数据，我们通过实验验证：在该模型下，用户分担多样化负担，且约束条件下效用损失极小。