Content creators compete for user attention. Their reach crucially depends on algorithmic choices made by developers on online platforms. To maximize exposure, many creators adapt strategically, as evidenced by examples like the sprawling search engine optimization industry. This begets competition for the finite user attention pool. We formalize these dynamics in what we call an exposure game, a model of incentives induced by algorithms including modern factorization and (deep) two-tower architectures. We prove that seemingly innocuous algorithmic choices -- e.g., non-negative vs. unconstrained factorization -- significantly affect the existence and character of (Nash) equilibria in exposure games. We proffer use of creator behavior models like ours for an (ex-ante) pre-deployment audit. Such an audit can identify misalignment between desirable and incentivized content, and thus complement post-hoc measures like content filtering and moderation. To this end, we propose tools for numerically finding equilibria in exposure games, and illustrate results of an audit on the MovieLens and LastFM datasets. Among else, we find that the strategically produced content exhibits strong dependence between algorithmic exploration and content diversity, and between model expressivity and bias towards gender-based user and creator groups.
翻译:内容创建者竞争用户的注意力。他们的影响力主要取决于在线平台开发者作出的算法选择。为了最大限度地扩大曝光,许多创建者从战略角度调整,例如,扩展搜索引擎优化产业。这为有限的用户关注池带来了竞争。我们在我们称之为曝光游戏中将这些动态正式化,这是由包括现代因数化和(深)双塔结构在内的算法引发的激励模式。我们证明,似乎没有意义的算法选择 -- -- 例如,在曝光游戏中,非负负对非不受限制的因数化 -- -- 极大地影响(Nash)平等游戏的存在和性质。我们利用像我们这样的创建者行为模型进行(前)部署前审计。这种审计可以发现理想内容和激励内容之间的不匹配,从而补充内容过滤和调适度等热后措施。为此,我们提出了在曝光游戏中寻找基于数字的平衡的工具,并展示了对电影Lens和LastFMD型数据设置中(Last FFM)等数据配置中存在差异的审计结果和性质。此外,我们发现,我们使用像我们这样的创造者行为模式和创造者之间具有很强的可靠性,我们发现,从战略上看,从性别上看,从性别上看,从性别上看,从性别上看,从性别上看,从性别上看,从性别分析。